Reviewerly/exHarmony huggingface dataset:
Nominal unification with atom-variables

The problem of nominal unification where variables are allowed for atoms, and computing a complete set of unifiers is considered. The complexity is shown to be NP-complete, while for special cases there are polynomial time algorithms. The main result is a novel algorithm to compute a complete set of unifiers which performs lazy guessing of equality or disequality of atom-variables, runs in NP time, and the collecting variant has more chances to keep the complete set of unifiers small. Applications of this algorithm are in reasoning about program transformations in higher order functional languages. We also present a variant of the unification algorithm that delays guessing and checking solvability, and produces a single most general unifier.
CTS: An operating system CPU scheduler to mitigate tail latency for latency-sensitive multi-threaded applications

Large-scale interactive Web services break a user’s request to many sub-requests and send them to a large number of independent servers so as to consult multi-terabyte datasets instantaneously. Service responsiveness hinges on the slowest server, making the tail of the latency distribution of individual servers a matter of great concern. A large number of latency-sensitive applications hosted on individual servers use thread-driven concurrency model wherein a thread is spawned for each user connection. Threaded applications rely on the operating system CPU scheduler for determining the order of thread execution. Our experiments show that the default Linux scheduler (CFS) idiosyncrasies result in LCFS (Last Come First Served) scheduling of threads belonging to the same application. On the other hand, studies have shown that FCFS (First Come First Served) scheduling yields the lowest response time variability and tail latency, making the default scheduler of Linux a source of long tail latency for multi-threaded applications. In this paper, we present CTS, an operating system CPU scheduler to trim the tail of the latency distribution for latency-sensitive multi-threaded applications while maintaining the key characteristics of the default Linux scheduler (e.g., fairness). By adding new data structures to the Linux kernel, CTS tracks threads belonging to an application in a timely manner and schedules them in FCFS manner, mitigating the tail latency. To keep the existing features of the default Linux scheduler intact, CTS keeps CFS responsible for system-wide load balancing and core level process scheduling; CTS merely schedules threads of the CFS chosen process in FCFS order, ensuring tail latency mitigation without sacrificing the default Linux scheduler properties. Experiments with a prototype implementation of CTS in the Linux kernel demonstrate that CTS significantly outperforms the Linux default scheduler. For example, CTS mitigates the tail latency of a Null RPC server by up to 96%, a Thrift server by up to 90% and an Apache Web server by up to 51% at 99.9th percentile.
Supergraph Search in Graph Databases via Hierarchical Feature-Tree

Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the efficiency and effectiveness of our algorithms.
Hidden the true identity and dating characteristics based on quick private matching in mobile social networks

With the massive explosion development of smart-phones and social networks usage, users can find potential friends who have similar characteristics by sharing personal similar characteristics attribute profile in mobile social networks (MSNs). However, the personal attribute profile usually contains sensitive information, and if this information is captured by attackers, it may cause unexpected consequences. In this paper, we propose a novel privacy-preserving matching scheme which is based on both identity authentication and private matching. The scheme relies on trusted third party which has powerful computation ability and can ease the workload on intelligent terminal. Moreover, the scheme uses encryption and authentication techniques to guarantee that the attacker fails to get the real information of user’s attribute profile, so the personal privacy can be protected during friend matching process. Security analysis shows that the proposed scheme can protect the user’s privacy. The simulation result shows that the scheme is more efficient than existing works.
A quality evaluation model for calculating block and blur effects generated by H.264 and MPEG2 codecs

Many factors can affect and/or compromise the quality of visual media. Most video compression standards use motion compensation and block-based encoding scheme for compression. As a result, the decoded video is altered by one or more compression artifacts, such as block effect, blur, color bleeds, buzzing, false edges, jagged motion and flickering chrominance. In this framework, we present in this paper a quality prediction model for video encoders. Our model is designed to calculate the distortions of blocking and blurring effects, which appeared from the coding operation. We also give an overall score of the two distortions. The validation of our model is done using the subjective video database “LIVE” to correlate with the judgment of the observers. Correlation indicators such as the Spearman rank correlation coefficient (SROCC) and the Pearson linear correlation coefficient (PLCC) indicate that our model gives good performance.
Extraction of Formal Manufacturing Rules from Unstructured English Text

Semantics-based approaches – founded on the idea of explicitly encoding meaning separately from the data or the application code – are being applied to manufacturing, for example, to enable early manufacturability feedback. These approaches rely on formal, i.e., computer-interpretable, knowledge and rules along with the context or semantics, which facilitates the reuse and sharing of the knowledge via semantic web technologies. On the other hand, manufacturing knowledge has been maintained primarily in the form of unstructured English text. It is considered impractical for engineers to author accurate, formal, and structured manufacturing rules. However, previous efforts on extracting semantics from unstructured text in manufacturing have mainly focused on basic concept names and hierarchies for ontology creation, rather than extracting complex manufacturing rules. In this context, this paper focuses on the development of a semantics-based framework for acquiring formal manufacturing rules from English text, such as those written in manufacturing handbooks, by guiding standard Natural Language Processing (NLP) techniques with formal manufacturing knowledge (i.e., controlled vocabulary and domain ontology). Specifically, this paper studies the problem of rule extraction in the manufacturing domain, proposes the formal rule extraction framework, and demonstrates its feasibility. From the dataset of 133 sentences with a manufacturing rule, the proposed framework was able to extract correct rules from approximately 57% of the sentences. This paper also demonstrates the extensibility of the framework. Specifically, the framework was initially developed using the three sections of a manufacturing handbook, including milling, metal stamping, and die-casting sections, and could be successfully applied to the rest of the book after just updating the formal manufacturing knowledge to cover the other sections. This paper provides meaningful results in terms of formalization, thus will contribute to the development, sharing, and reuse of formal manufacturing knowledge that includes complex manufacturing rules.
User-centric pattern mining on knowledge graphs: An archaeological case study

In recent years, there has been a growing interest from the digital humanities in knowledge graphs as data modelling paradigm. Already, this has led to the creation of many such knowledge graphs, many of which are now available as part of the Linked Open Data cloud. This presents new opportunities for data mining. In this work, we develop, implement, and evaluate (both data-driven and user-driven) an end-to-end pipeline for user-centric pattern mining on knowledge graphs in the humanities. This pipeline combines constrained generalized association rule mining with natural language output and facet rule browsing to allow for transparency and interpretability—two key domain requirements. Experiments in the archaeological domain show that domain experts were positively surprised by the range of patterns that were discovered and were overall optimistic about the future potential of this approach.
A Highly Integrated Hardware/Software Co-Design and Co-Verification Platform

This article presents a platform for hardware/software co-design and co-verification with a flexible hardware/software interface. The platform has been applied to verification of a pedestrian tracking application to demonstrate its effectiveness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–Wen Chen, NXP Semiconductors NV</i>
MicroBlaze-Based Multiprocessor Embedded Cryptosystem on FPGA for Elliptic Curve Scalar Multiplication Over Fp

In this paper, we present Microblaze-based parallel architectures of Elliptic Curve Scalar Multiplication (ECSM) computation for embedded Elliptic Curve Cryptosystem (ECC) on Xilinx FPGA. The proposed implementations support arbitrary Elliptic Curve (EC) forms defined over large prime field ([Formula: see text]) with different security-level sizes. ECSM is performed using Montgomery Power Ladder (MPL) algorithm in Chudnovsky projective coordinates system. At the low abstraction level, Montgomery Modular Multiplication (MMM) is considered as the critical operation. It is implemented within a hardware Accelerator MMM (AccMMM) core based on the modified high radix, [Formula: see text] MMM algorithm. The efficiency of our parallel implementations is achieved by the combination of the mixed SW/HW approach with Multi Processor System on Programmable Chip (MPSoPC) design. The integration of multi MicroBlaze processor in single architecture allows not only the flexibility of the overall system but also the exploitation of the parallelism in ECSM computation with several degrees. The Virtex-5 parallel implementations of 256-bit and 521-bis ECSM computations run at 100[Formula: see text]MHZ frequency and consume between 2,739 and 6,533 slices, 22 and 72 RAMs and between 16 and 48 DSP48E cores. For the considered security-level sizes, the delays to perform single ECSM are between 115[Formula: see text]ms and 14.72[Formula: see text]ms.
Modeling and Visualizing Student Flow

In this work, we present a data science system to model and visualize student flow patterns based on electronic student data of a university. Our system is called eCamp. The datasets used by eCamp were previously disconnected and only maintained and accessed in a siloed manner by independent campus offices. At a campus-level, our models and visualization show how students make choices among hundreds of potential majors, as students gradually progress towards their sophomore, junior, and senior year. At a department-level, the student flow patterns revealed by eCamp show how each course plays a different role within a curriculum. eCamp further dives down to the granularity of the exact classes offered in each semester. At that level, eCamp shows how students navigate from one set of classes in one semester to another set in a subsequent semester. Previously, comprehensive information about student progression patterns at all of these level was simply unavailable. To that end, we also demonstrate how insights into such student flow patterns can support analytical tasks involving student outcomes, student retention, and curriculum design.
Combining predictors for multi-layer architecture of adaptive fuzzy inference system

To solve problems with a Sugeno adaptive fuzzy neural network using training data, it is necessary to select the appropriate combination of input characteristics of the sub-adaptive neuro-fuzzy inference system (ANFIS) and to determine the appropriate topology. The multi-layer architecture of a sub-ANFIS (MLA-ANFIS) is a good model for prediction problems and solves them modularity. Since, the combination of several predictors is the current focus in the construction of hybrid intelligent systems; we created many solutions to combine machine learning methods, namely ANFIS, support vector machine (SVM), deep neural network (DNN), naive Bayes (NB), linear regression (LR), extreme learning machine (ELM), and decision tree (DT) mixed predictors, and ensemble bootstrap aggregation based on MLA-ANFIS in order to discover the optimal model of combined predictors based on the MLA-ANFIS with a combination of input features entered in the MLA-ANFIS. We implemented our approaches on 365-day concrete compressive strength, thoracic surgery, fertility diagnosis, breast, energy, and glass identification datasets from UCI. The experimental results prove that the combining predictors for the MLA-ANFIS show performance improvements compared to the pure MLA-ANFIS method.
iFlask: Isolate flask security system from dangerous execution environment by using ARM TrustZone

Security is essential in mobile computing. And, therefore, various access control modules have been introduced. However, the complicated mobile runtime environment may directly impact on the integrity of these security modules, or even compels them to make wrong access control decisions. Therefore, for a trusted Flask based security system, it needs to be isolated from the dangerous mobile execution environment at runtime. In this paper, we propose an isolated Flask security architecture called iFlask to solve this problem for the Flask-based mandatory access control (MAC) system. iFlask puts its security server subsystem into the enclave provided by the ARM TrustZone so as to avert the negative impacts of the malicious environment. In the meanwhile, iFlask’s object manager subsystems which run in the mobile system kernel use a built-in supplicant proxy to effectively lookup policy decisions made by the back-end security server residing in the enclave, and to enforce these rules on the system with trustworthy behaviors. Moreover, to protect iFlask’s components which are not protected by the enclave, we not only provide an exception trap mechanism that enables TrustZone to enlarge its protection scope to protect selected memory regions from the malicious system, but also establish a secure communication channel to the enclave as well. The prototype is implemented on SELinux, which is the widely used Flask-based MAC system, and the base of SEAndroid. The experimental results show that SELinux receives reliable protection, because it resists all known vulnerabilities (e.g., CVE-2015-1815) and remains unaffected by the attacks in the test set. The propose architecture have very slight impact on the performance, it shows a performance degradation ranges between 0.53% to 6.49% compared to the naked system.
Support vector neural network based fuzzy hybrid filter for impulse noise identification and removal from gray-scale image

Denoising is an indispensable task to restore the image features from the corrupted low-quality images and improve the perceptual quality of images. Besides significant advantages in the field of the denoising images, several kinds of literature face difficulties in reducing the impulse noise in the image. This work designs a novel filter through the hybridization of the fuzzy filter and the Non-Local Means (NLM) filter. The proposed scheme removes the impulse noise in the images in two stages 1) noise identification, and 2) denoising stage. Noise identification is made by constructing the binary map based on the Support Vector Neural Network (SVNN) classifier. The SVNN classifier is trained based on the Genetic Algorithm (GA) for identifying the optimal weights and the bias. The features for the training are extracted from the images, and thus, the training procedure differentiates the noisy pixel from the good pixel. At the denoising stage, the hybrid filter is enabled to remove the impulse noise in the image. The proposed model uses the five standard images, such as Baboon, cameraman, Lena, peppers, and Pemaquid Point Lighthouse image for the experimentation purpose. From the simulation results, it is evident that the proposed hybrid filter along with the SVNN classifier achieved improved results with the values of 47.278 dB, 0.978 and 61.637 dB for the PSNR, SSIM, and the SDME, respectively.
Improvement on Image Edge Detection Using a Novel Variant of the Ant Colony System

In this paper, an improved Ant Colony System algorithm applied to image edge detection is presented. During their movement on image, artificial ants establish pheromone graph which represents the image edge information. The ant movement is directed by the local variation of the image’s intensity values. To improve this method, supplementary behaviors are added to each ant in response to its local stimuli, i.e., the ant self-reproduces and directs its progenitors to an appropriate direction to explore more suitable areas. Moreover, it dies if it exceeds a specific iteration age and so the ineffective searches are eliminated. These additional behaviors allow diversifying the exploration performed by ants and also reinforcing the exploitation of these ants’ search experience. Proposed approach allows having more accurate and more complete edges. The performance is tested visually with various images and empirically with evaluation parameters.
Efficient Design of Quantum Circuits Using Nearest Neighbor Constraint in 3D Architecture

Synthesis and optimization of quantum circuits have received significant attention from researchers in recent years. Developments in the physical realization of qubits in quantum computing have led to new physical constraints to be addressed. One of the most important constraints that is considered by many researchers is the nearest neighbor constraint which limits the interaction distance between qubits for quantum gate operations. Various works have been reported in the literature that deal with nearest neighbor compliance in multi-dimensional (mostly 1D and 2D) qubit arrangements. This is normally achieved by inserting SWAP gates in the gate netlist to bring the interacting qubits closer together. The main objective function to minimize here is the number of SWAP gates. The present paper proposes an efficient qubit placement strategy in a three-dimensional (3D) grid that considers not only qubit interactions but also the relative positions of the gates in the circuit. Experimental evaluation on a number of benchmark circuits show that the proposed method reduces the number of SWAP gates by 16.2% to 47.0% on the average as compared to recently published works.
Software Development Analytics for Xen: Why and How

Xen is one of the most popular virtualization technologies. Several IT companies collect and publish metrics, helping the ecosystem to be more self-aware of their development processes. Thus, ecosystem participants make informed decisions, monitor their effects, and improve their coordination.
Performance analysis of the reserve capacity policy for dynamic VM allocation in a SaaS environment

We consider a periodic-review provision scheme with constant inspection intervals for allocating dynamically virtual machines (VMs) in a Software-as-a Service (SaaS) environment. At each interval, we determine how many virtual machines (VMs) to provisioned or de-provision using a simple heuristic referred to as the reserve capacity policy, since it maintains a fixed reserve capacity of VMs. We analyze the performance of the reserve capacity policy within the context of a periodic-review provision scheme using a Markov Chain embedded at the inspection intervals. We assume a single stream of jobs with each job requiring a single VM. Jobs arrive in a Poisson fashion and the execution time of a job in a VM is exponentially distributed. We calculate the probability distribution of the number of customers in the system, the number of in-service VMs, the utilization, and the queue-length distribution of the waiting customers. The embedded Markov Chain is solved numerically. For cases where the underlying transition matrix is very large, we have proposed approximations and showed that they have a root mean square error (RMSE) of less than 2%.
Low Complexity, Hardware-Efficient Neighbor-Guided SGM Optical Flow for Low-Power Mobile Vision Applications

Accurate, low-latency, and energy-efficient optical flow estimation is a fundamental kernel function to enable several real-time vision applications on mobile platforms. This paper presents neighbor-guided semi-global matching (NG-fSGM), a new low-complexity optical flow algorithm tailored for low-power mobile applications. NG-fSGM obtains high accuracy optical flow by aggregating local matching costs over a semiglobal region, successfully resolving local ambiguity in textureless and occluded regions. The proposed NG-fSGM aggressively prunes the search space based on neighboring pixels' information to significantly lower the algorithm complexity from the original fSGM. As a result, NG-fSGM achieves 17.9× reduction in the number of computations and 8.37× reduction in memory space compared to the original fSGM without compromising its algorithm accuracy. A multicore architecture for NG-fSGM is implemented in hardware to quantify algorithm complexity and power consumption. The proposed architecture realizes NG-fSGM with overlapping blocks processed in parallel to enhance throughput and to lower power consumption. The eightcore architecture achieves 20 M pixel/s (66 frames/s for VGA) throughput with 9.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area at 679.2-mW power consumption in 28-nm node.