Reviewerly/exHarmony huggingface dataset:
Suspicious-taint-based access control for protecting OS from network attacks

Today, security threats to operating systems largely come from network. Traditional discretionary access control mechanism alone can hardly defeat them. Although traditional mandatory access control models can effectively protect the security of OS, they have problems of being incompatible with application software and complex in administration. In this paper, we propose a new model, Suspicious-Taint-Based Access Control (STBAC) model, for defeating network attacks while being compatible, simple and maintaining good system performance. STBAC regards the processes using Non-Trustable-Communications as the starting points of suspicious taint, traces the activities of the suspiciously tainted processes by taint rules, and forbids the suspiciously tainted processes to illegally access vital resources by protection rules. Even in the cases when some privileged processes are subverted, STBAC can still protect vital resources from being compromised by the intruder. We implemented the model in the Linux kernel and evaluated it through experiments. The evaluation showed that STBAC could protect vital resources effectively without significant impact on compatibility and performance.
A hybrid watermarking scheme with CS theory for security of multimedia data

A hybrid watermarking scheme for multimedia data such as digital image, digital video is proposed in this paper. The scheme utilizes various image processing transforms and Compressive Sensing (CS) to achieved fragility and security for multimedia data. The compressive sensing is applied on the singular value of wavelet coefficients of watermark image to get the CS measurements. These CS measurements are embedded with embedding factor into the hybrid coefficients (high frequency curvelet coefficients of DT coefficients) of the host medium. In the proposed scheme, host medium may be digital image or video. The generated watermarked data have different security layers such as four levels from CS such as knowledge of transform basis matrix, measurement matrix, orthogonal matrices, information of wavelet basis matrix and one level from watermarking such as embedding factor. The scheme offers fragility and security to multimedia data against various standard watermarking attacks such as signal processing attacks, geometric attacks, and compression attacks. This scheme can be provided high payload capacity and used for host multimedia authentication. A comparison of proposed scheme with existed schemes shows that the proposed scheme performed faster and better than existed schemes available in the literature.
Visual Guidance for a Spatial Discrepancy Problem of in Encountered-Type Haptic Display

In virtual environments, spatial discrepancies between visual and haptic scenes negatively impact user performance and experience. This paper shows how spatial discrepancies due to pose differences can occur in a haptic augmented virtuality system with an encountered-type haptic display. To mitigate this problem, we propose visual guidance, an algorithm that dynamically manipulates the visual scene to compensate for discrepancies. The effectiveness of this algorithm was verified in a pair of studies involving a button pressing task and spatial discrepancies between ±150 mm and ±40°. Experimental results show that discrepant trials using the technique yield error rates and a number of speed peaks (representing the number of targeting movements) that are comparable to those attained in trials with zero spatial discrepancy. This result was also achieved without requiring a dedicated adaptation or training process, ensuring the algorithm can be used immediately by users. A pair of follow-up studies also indicates the algorithm has little impact on subjective ratings of simulator sickness, suggesting that sporadic use of the algorithm will not negatively affect user's experience of a virtual environment. We believe that the visual guidance algorithm presented in this paper can be used to create more useful and compelling experiences in various haptic training applications incorporating encountered-type haptic displays.
Relationships between proof and examples: Comments arising from the papers in this issue

The papers in this issue provide an excellent example of how pertinent distinctions which emerge from analysis of significant data sets can be communicated to colleagues and used to make sense of observations. The distinctions are vividly described, illustrated and exemplified, and used to chart maturation in student use of examples from middle school, through high school and into university. Curiously, no corresponding maturation was detected in students’ choice of examples to use for themselves. Contrast was detected between the way students used the same examples for comprehending a conjecture and testing its validity, and the way that some mathematicians used specifically constructed examples for locating underlying structure. What emerged for me from reading these papers is confirmation of the central role of attention, the productiveness of engaging students at the earliest ages and throughout their schooling with the detection and expression of generality, and the importance of being explicit about the scope of generality encompassed by a claim. Purposeful use of examples by students could be expected to develop as teachers explicitly use examples purposefully with and for students. A reasonable conjecture might be that getting students to be explicit about the scope of generality, of possible variation of which they are aware, might shed light on obstacles and barriers to reasoning which might otherwise lead to proof.
A Generalized Framework For Kullback–Leibler Markov Aggregation

We propose an information-theoretic Markov aggregation framework that is motivated by two objectives: 1) The Markov chain observed through the aggregation mapping should be Markov. 2) The aggregated chain should retain the temporal dependence structure of the original chain. We analyze our parameterized cost function and show that it contains previous cost functions as special cases, which we critically assess. Our simple optimization heuristic for deterministic aggregations characterizes the optimization landscape for different parameter values.
ELM-Based Large-Scale Genetic Association Study via Statistically Significant Pattern

Genetic association study (GAS) is a promising tool for detecting and analyzing the cause of complex diseases. The extreme learning machine (ELM) has been successfully applied in a variety of research fields. Yet, as a black box method, it could not measure up to the task of GAS by itself, because it cannot tell us what causes the diseases, which is very crucial to the biologists. In this paper, we propose an ELM-based statistically significant pattern classification framework, which combines ELM with feature vector-based methods to solve the GAS problem efficiently and effectively. In particular: 1) a statistically significant pattern considering in terms of both family wise error rate (FWER) and false discovery rate (FDR) is proposed to control false positives in multiple hypothesis tests, which is necessary in GAS, but ignored by most of the existing methods; 2) an upper bound of the significance of a pattern is deduced to speed up FWER-constrained statistically significant pattern mining in a row enumeration way. Further, a space-effective grid index is devised to dramatically improves the efficiency of FDR-constrained pattern discovery; and 3) an ELM classifier is constructed based on the significant patterns. Comprehensive empirical studies on four real genotype datasets demonstrate much higher efficiency and effectiveness of our proposed framework with respect to the compared methods.
Transfer Function-Guided Saliency-Aware Compression for Transmitting Volumetric Data

We introduce a transfer-function-guided three-dimensional (3-D) block-based saliency-aware compression scheme for volumetric data that is both content and spatially scalable. Salient 3-D volumetric blocks are identified and weighted with the help of a transfer function which is used to render the data. We describe our method in the form of a framework for processing, progressive transmission, and visualization of volumetric data on a target device, such as a mobile device with limited computational resources. In particular, we address the transmission bottleneck incurred when transferring 3-D volumetric data. Identified salient regions are progressively transmitted to the target device. The received data are rendered progressively in the respective order with a predefined or user-defined transfer function. Our method is developed with medical applications in mind, where preservation of all information is essential for clinical diagnosis. Because our method is integrated into a resolution scalable coding scheme with an integer wavelet transform of the image, it allows the rendering of each significant region at a different resolution up to fully lossless reconstruction. We perform a thorough qualitative and quantitative evaluation of the saliency detection method and the resulting saliency-aware compression schemes. Our results show reduced error in representation of the volumetric data with our method.
In-place initializable arrays

An initializable array is an array that supports the read and write operations for any element and the initialization of the entire array. This paper proposes a simple in-place algorithm to implement an initializable array of length N containing ℓ∈O(w) bits entries in Nℓ+1 bits on the word RAM model with w bits word size, i.e., the proposed array requires only 1 extra bit on top of a normal array of length N containing ℓ bits entries. Our algorithm supports the all three operations in constant worst-case time, that is, it runs in-place using at most constant number of words O(w) bits during each operation. The time and space complexities are optimal since it was already proven that there is no implementation of an initializable array with no extra bit supporting all the operations in constant worst-case time (Hagerup and Kammer, 2017 [12]). Our algorithm significantly improves upon the best algorithm presented in the earlier studies (Navarro, 2014 [5]) which uses N+o(N) extra bits to support all the operations in constant worst-case time.
A decision procedure and complete axiomatization for projection temporal logic

To specify and verify the concurrent and reactive systems with the theorem proving approach, a complete axiomatization is formalized for first order projection temporal logic (PTL) with both finite and infinite time. To this end, PTL is restricted to a finite domain, and the syntax, semantics as well as the axiomatization of PTL are presented. Further, the techniques of labeled normal form and labeled normal form graph of PTL formulas are introduced respectively, with which a decision procedure for quantifier free PTL (QFPTL) formulas is given. Moreover, a generalized labeled normal form graph is defined and employed to transform a quantified PTL formula into its equivalent QFPTL formula. Finally, a decision procedure for PTL is formalized and the completeness of the axiomatic system is proved based on the decidability of PTL formulas.
FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization

Knowledge graphs have received intensive research interests. When the labels of most nodes or datapoints are missing, anchor graph and hierarchical anchor graph models can be employed. With an anchor graph or hierarchical anchor graph, we only need to optimize the labels of the coarsest anchors, and the labels of datapoints can be inferred from these anchors in a coarse-to-fine manner. The complexity of optimization is therefore reduced to a cubic cost with respect to the number of the coarsest anchors. However, to obtain a high accuracy when a data distribution is complex, the scale of this anchor set still needs to be large, which thus inevitably incurs an expensive computational burden. As such, a challenge in scaling up these models is how to efficiently estimate the labels of these anchors while keeping classification performance. To address this problem, we propose a novel approach that adds an anchor label predictor in the conventional anchor graph and hierarchical anchor graph models. In the proposed approach, the labels of the coarsest anchors are not directly optimized, and instead, we learn a label predictor which estimates the labels of these anchors with their spectral representations. The predictor is optimized with a regularization on all datapoints based on a hierarchical anchor graph, and we show that its solution only involves the inversion of a small-size matrix. Built upon the anchor hierarchy, we design a sparse intra-layer adjacency matrix over these anchors, which can simultaneously accelerate spectral embedding and enhance effectiveness. Our approach is named Faster Learning on Anchor Graph (FLAG) as it improves conventional anchor-graph-based methods in terms of efficiency. Experiments on a variety of publicly available datasets with sizes varying from thousands to millions of samples demonstrate the effectiveness of our approach.
Google Users as Sequences: A Robust Hierarchical Cluster Analysis Study

In this era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. By understanding user behavior in constructing applications and interacting with typical cloud infrastructures, cloud managers can develop better systems that improve the users' experience. In this paper, we analyze a large dataset of a Google cluster to characterize the users into distinct groups of similar usage behavior. We used a wide range of measured metrics to model user behavior in composing applications from the perspective of actions around application architecting, capacity planning, and workload type planning and to model user interaction behavior around the session view. The trajectories of users' actions are represented as sequences using categorical and proportional encoding schemes. We used techniques from the sequence analysis paradigm to quantify dissimilarity among users. We employed a robust cluster analysis procedure based on the agglomerative hierarchical methods to optimally classify users into 12 classes. We used a variety of formal indices and visual aids to confirm the quality and stability of the outcomes. By visual inspection, we regrouped the obtained clusters into 5 main groups that reveal interesting insights about the characteristics which underline different groups' utilization behavior.
Infrastructures for High-Performance Computing: Cloud Infrastructures

This article describes the main services provided by the most popular cloud infrastructures currently in use. The systems presented in this article are either public clouds Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and IBM Bluemix or private clouds OpenStack, OpenNebula and Eucalyptus. The formers provide a variety of services that implement all the three main cloud service models (IaaS, PaaS and SaaS), while the latters are focused on the IaaS model, as they provide services for the management of large pools of processing, storage, and networking resources in a data center, by exploiting a variety of virtualization techniques.
Sparse-to-Dense Depth Estimation in Videos via High-Dimensional Tensor Voting

Due to the popularity of 3D videos, 2D-to-3D video conversion has become a hot research topic for the past few years. The most critical issue in 3D video synthesis is the estimation of depth maps for the video frames. Numerous efforts have been devoted in fully automatic and semi-automatic depth estimation approaches, although the discontinuity of depth field and the ambiguity of motion boundary are still the main challenges in depth estimation. This paper proposes a semi-automatic structure-aware sparse-to-dense depth estimation method, which leverages the tensor voting at two different levels to propagate depth across frames. In the first level, a 4D tensor voting is performed to remove outliers caused by inaccurate motion estimation. Noticing that the 4D tensors of correctly matched points should lie on the smooth layer in the manifold, we utilize the variety saliency defined by the eigen-system of the tensor for outlier removal. In the second level, a high-dimensional tensor voting algorithm, incorporating spatial location, motion, and color into the tensor representation, is devised to propagate the depth from the sparse points to the entire image domain. By projecting the input feature into the tangent space, the relation between the location, motion, color, and the depth can be established by voting process. Extensive experiments on public data set validate the effectiveness of the proposed method in comparison with state-of-the-art depth estimation approaches.
Secure Dependency Enforcement in Package Management Systems

Package management systems play an essential role in pursuing systems dependability by ensuring that software is correctly installed and kept up-to-date according to vendor-defined installation policies. Circumventing such policies could make the system unhealthy and insecure and can constitute a serious security threat. In many application scenarios, e.g., distribution of commercial software, the confidentiality of the software must be guaranteed against non-authorized players. In some cases, the installation policy itself is considered a sensitive information, e.g., when it reveals required hardware in military contexts. In this paper we address the problem of strongly enforcing software dependencies in package management systems, to prevent that a malicious user forces the system to install any package despite its requirements are not completely fulfilled. The enforcement is strong in the sense that the encrypted software package cannot be even decrypted if the dependencies are not satisfied. Once a new package is decrypted and installed, our protocol non-interactively updates the key material on the target device. This key update will allow the decryption of further packages that depend on the newly installed one. We further present “policy-hiding” variants of our protocol. Finally we provide an experimental evaluation of the system performance.
Declarative Construction of Distributed Event-driven IoT Services Based on IoT Resource Models

In Internet of Things (IoT)scenarios, the coordination of physical systems is often complex and rigid. Work to date has not comprehensively explored how to flexibly construct distributed IoT services to satisfy the coordination requirement. In our work, we propose a declarative approach to construct an event-driven IoT service system, where physical devices and systems are explicitly modeled as a service architecture foundation, the service behavior is flexibly declared based on distributed events, and it is step-by-step refined to have rigid service properties while allowing runtime adaptation. As an example, the controllability property of physical systems is discussed. Finally, we establish a distributed event-driven IoT service platform to test our method. Some experiments are performed to concept-prove our work.
Annotation paths for matching XML-Schemas

Annotation paths are a technique for the semantic annotation of XML-Schemas. The design rationale was to develop an embedded annotation method on top of SAWSDL which is fully declarative, easily applicable and still provides the proper expressiveness for high-quality logic-based schema matching. Annotation paths capture significantly more semantics than plain model references, the declarative annotation method of the W3C standard SAWSDL. While the concept of annotation paths was introduced in earlier works, we provide a new formalization of their structure and based thereon define their semantics and introduce matching methods to derive simple and complex value correspondences. Such correspondences can be used for the generation of executable schema mappings using state of the art mapping tools. We provide a comprehensive evaluation of our annotation method and the proposed matching algorithms using real-world schemas and reference ontologies and demonstrate the feasibility of generating executable mappings using a state of the art mapping system. Our evaluations show that our annotation-based matcher achieves outstanding matching quality (avg. f-measure between 0.98 and 1.0).
Exploiting the structure of furthest neighbor search for fast approximate results

We present a novel strategy for approximate furthest neighbor search that selects a set of candidate points using the data distribution. This strategy leads to an algorithm, which we call DrusillaSelect, that is able to outperform existing approximate furthest neighbor strategies. Our strategy is motivated by a study of the behavior of the furthest neighbor search problem, which has significantly different structure than the nearest neighbor search problem, and can be understood with the help of an information-theoretic hardness measure that we introduce. We also present a variant of the algorithm that gives an absolute approximation guarantee; under some assumptions, the guaranteed approximation can be achieved in provably less time than brute-force search. Performance studies indicate that DrusillaSelect can achieve comparable levels of approximation to other algorithms, even on the hardest datasets, while giving up to an order of magnitude speedup. An implementation is available in the mlpack machine learning library (found at http://www.mlpack.org).
A Tool for Generating Health Applications Using Archetypes

Template4EHR is a to ol for the dynamic creation of data schemas for electronichealth-record storage and user creation and customization of graphical user interfaces. In experimental tests with IT and health professionals, Template4EHR obtained an 81.22% satisfaction rate.
Body Parts Synthesis for Cross-Quality Pose Estimation

Although encouraging results have been obtained in human pose estimation in recent years, the performance may degrade dramatically when the image quality differs between training and testing data sets. This paper addresses problems in cross-image-quality human pose estimation. To achieve this, we follow an unsupervised domain adaptation approach, in which labels in the target domain are unavailable. Unlike existing unsupervised domain adaptation methods that find label information from unlabeled data, the target pose information (label) is instead generated by synthesizing body parts with similar image-quality of the target domain. A translative dictionary is learned to associate the source and target domains, and a cross-quality adaptation model is developed to refine the source pose estimator using the synthesized target body parts. We perform cross-quality experiments on three data sets with different image quality by using two state-of-the-art pose estimators, and compare the proposed method with five unsupervised domain adaptation methods. Our experimental results show that the proposed method outperforms not only the source pose estimators, but also other unsupervised domain adaptation methods.