Reviewerly/exHarmony huggingface dataset:
Privacy-preserving matrix product based static mutual exclusive roles constraints violation detection in interoperable role-based access control

Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.
Complete Online Algorithm for Air Data System Calibration

Air data systems require costly calibration of their static pressure sensors to characterize errors caused by the act of flying. Altitude-based methods for measuring these so-called static position errors, such as the tower flyby, produce accurate results but require an elaborate flyby site, multiple experiments to capture the relationship between error and airspeed, and are limited to subsonic airspeeds due to inherent hazards to land-based and aircraft structures from low-altitude supersonic flight. Airspeed-based methods using the Global Positioning System (GPS) are generally easier to execute, but they tend to yield less precise results and still require multiple experiments. Additionally, they require temperature probe calibration from external sources. This paper proposes a self-contained online method for complete air data calibration. The proposed method uses a Kalman smoother to fuse GPS altitude and airspeed measurements, aircraft attitude, and air data to produce the full static position error curve as a function of Mach number in a single experiment, with no need for external temperature calibration and with no supersonic limitations. The proposed method is validated using T-38C flight data, and it is shown to reduce cost by 88% while modeling a 42% larger domain when compared to current methods.
Robotic Assembly Planning of Tensegrity Structures

Tensegrity structures provide durability and stability with minimal weight. Thanks to these properties, use of tensegrity structures for space applications is becoming an active research area. One of the main challenges is the automated construction of these structures. In this work, we present a framework for the automated assembly planning of tensegrity structures using an industrial robot equipped with a purpose-specific end-effector. We leverage the recent advances in sampling-based motion planning to create the motion strategies. Specifically, we divided the assembly planning problem into three stages. In the first stage, the initial position and orientation of the given tensegrity structure with respect to the assembly robot is determined using a motion-planning integrated forward elimination search. In the second stage, a feasible assembly sequence of the strings is found using backward disassembly search. Lastly, the individual robot motion plans for attaching each string to the bars for the given tensegrity configuration is generated using RRT*. The efficacy of the framework was demonstrated using an extensive set of simulation experiments dealing with the assembly of a three-strut and nine-string tensegrity prism structure, which can be utilized as a building block of complex tensegrities.
Quantum sketching protocols for Hamming distance and beyond

In this work we use the concept of quantum fingerprinting to develop a quantum communication protocol in the simultaneous message passing model that calculates the Hamming distance between two $n$-bit strings up to relative error $\epsilon$. The number of qubits communicated by the protocol is polynomial in $\log{n}$ and $1/\epsilon$, while any classical protocol must communicate $\Omega(\sqrt{n})$ bits. Motivated by the relationship between Hamming distance and vertex distance in hypercubes, we apply the protocol to approximately calculate distances between vertices in graphs that can be embedded into a hypercube such that all distances are preserved up to a constant factor. Such graphs are known as $\ell_1$-graphs. This class includes all trees, median graphs, Johnson graphs and Hamming graphs. Our protocol is efficient for $\ell_1$-graphs with low diameter, and we show that its dependence on the diameter is essentially optimal. Finally, we show that our protocol can be used to approximately compute $\ell_1$-distances between vectors efficiently.
Construction of new Griesmer codes of dimension 5

We construct Griesmer [ n , 5 , d ] q codes for 2 q 4 − 3 q 3 + 1 ≤ d ≤ 2 q 4 − 3 q 3 + q 2 and for 3 q 4 + 5 q 3 + 1 ≤ d ≤ 3 q 4 + 5 q 3 + q 2 for every q ≥ 3 using some geometric methods such as projective dual and geometric puncturing.
Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

The digital medical workflow faces many circumstances in which the images can be manipulated during viewing, extracting and exchanging. Reversible and imperceptible watermarking approaches have the potential to enhance trust within the medical imaging pipeline through ensuring the authenticity and integrity of the images to confirm that the changes can be detected and tracked. This study concentrates on the imperceptibility issue. Unlike reversibility, for which an objective assessment can be easily made, imperceptibility is a factor of human cognition that needs to be evaluated within the human context. By defining a perceptual boundary of detecting the modification, this study enables the formation of objective guidelines for the method of data encoding and level of image/pixel modification that translates to a specific watermark magnitude. This study implements a relative Visual Grading Analysis (VGA) evaluation of 117 brain MR images (8 original and 109 watermarked), modified by varying techniques and magnitude of image/pixel modification to determine where this perceptual boundary exists and relate the point at which change becomes noticeable to the objective measures of the image fidelity evaluation. The outcomes of the visual assessment were linked to the images Peak Signal to Noise Ratio (PSNR) values, thereby identifying the visual degradation threshold. The results suggest that, for watermarking applications, if a watermark is applied to the 512×512 pixel (16 bpp grayscale) images used in the study, a subsequent assessment of PSNR=82dB or greater would mean that there would be no reason to suspect that the watermark would be visually detectable.
Estimating post-editing time using a gold-standard set of machine translation errors

With the improved quality of Machine Translation (MT) systems in the last decades, post-editing (the correction of MT errors) has gained importance in Computer-Assisted Translation (CAT) workflows. Depending on the number and the severity of the errors in the MT output, the effort required to post-edit varies from sentence to sentence. The existing Quality Estimation (QE) systems provide quality scores that reflect the quality of an MT output at sentence level or word level. However, they fail to explain the relationship between different types of MT errors and the required post-editing effort to correct them. We suggest a more informative approach to QE in which different types of MT errors are detected in a first step, which are then used to estimate post-editing effort in a second step. In this paper we define the upper boundary of such a system. We use different machine learning methods to estimate Post-Editing Time (PET) by using a gold-standard set of MT errors as features. We show that post-editing time can be estimated with high accuracy when all the translation errors in the MT output are known. Furthermore, we apply feature selection methods and investigate the predictive power of different MT error types on PET. Our results show that the same prediction performance can be achieved by only using a small subset of MT error types, indicating that successful two-step QE systems can be built with less effort in the future, by detecting only the error types with highest predictive power.
Query Translation for Cross-Lingual Search in the Academic Search Engine PubPsych

We describe a lexical resource-based process for query translation of a domain-specific and multilingual academic search engine in psychology, PubPsych. PubPsych queries are diverse in language with a high amount of informational queries and technical terminology. We present an approach for translating queries into English, German, French, and Spanish. We build a quadrilingual lexicon with aligned terms in the four languages using MeSH, Wikipedia and Apertium as our main resources. Our results show that using the quadlexicon together with some simple translation rules, we can automatically translate 85% of translatable tokens in PubPsych queries with mean adequacy over all the translatable text of 1.4 when measured on a 3-point scale [0, 1, 2].
Calibration of magnetic compass using an improved extreme learning machine based on reverse tuning

Purpose The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method. Design/methodology/approach A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed. Findings The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm. Originality/value MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.
IEGAN: Multi-Purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network

Despite the breakthroughs in quality of image enhancement, an end-to-end solution for simultaneous recovery of the finer texture details and sharpness for degraded images with low resolution is still unsolved. Some existing approaches focus on minimizing the pixel-wise reconstruction error which results in a high peak signal-to-noise ratio. The enhanced images fail to provide high-frequency details and are perceptually unsatisfying, i.e., they fail to match the quality expected in a photo-realistic image. In this paper, we present Image Enhancement Generative Adversarial Network (IEGAN), a versatile framework capable of inferring photo-realistic natural images for both artifact removal and super-resolution simultaneously. Moreover, we propose a new loss function consisting of a combination of reconstruction loss, feature loss and an edge loss counterpart. The feature loss helps to push the output image to the natural image manifold and the edge loss preserves the sharpness of the output image. The reconstruction loss provides low-level semantic information to the generator regarding the quality of the generated images compared to the original. Our approach has been experimentally proven to recover photo-realistic textures from heavily compressed low-resolution images on public benchmarks and our proposed high-resolution World100 dataset.
Asynchronous hardware implementations for crypto primitives

Cryptography plays a major role in all the modern applications, where the Galois field (GF) arithmetic circuits are inevitable. In this paper, asynchronous GF(2m) and m-bits GF(p) multiplier, inverter, and exponentiator are proposed, where the hardware is repeatedly reused for m iterations without synchronous registers (m=log2p). Also, this paper proposes an asynchronous implementation of GF(2163) affine coordinate based ECC scalar multiplication that includes the point addition and point doubling. Here, the inverse is calculated using Fermat's Little theorem. The entire scalar multiplication is done using only two GF(2163) multipliers without any hardware registers that are replaced by a completion detection logic. The same proposed logic is used in the asynchronous 128-bits AES design. The power dissipation of these proposed designs are much less than the existing designs due to the elimination of synchronous registers. Our proposed asynchronous logic is free from the glitches and metastability. The proposed asynchronous GF(216) multiplier design achieves 99.6% of improvement in switching power reduction than scalable Montgomery [5] based multiplier using 45 nm CMOS technology.
QoE-Oriented Multimedia Assessment: A Facial Expression Recognition Approach

Multimedia services are predominant in current wireless networks and are becoming ubiquitous in the upcoming 5G era in which the video quality of experience (QoE) is a fundamental metric. However, no widely accepted QoE model exists due to its subjective nature. This paper proposes a framework for quantifying the QoE of multimedia content based on the facial expression approach, which can directly reflect the end users’ intrinsic attitudes toward the services. To achieve this objective, a face database is established containing over one thousand videos and serves as a dataset for the subsequent experience mining. The semi-supervised clustering method proposed in this paper is applied to calculate the video experience scores and achieves 8% higher average test accuracy than other prevailing methods. Extensive experimental results show that our approach can accurately reveal the user's experience toward video contents and is expected to become a valid and useful QoE model.
Going Deeper With Semantics: Video Activity Interpretation Using Semantic Contextualization

A deeper understanding of video activities extends beyond recognition of underlying concepts such as actions and objects: constructing deep semantic representations requires reasoning about the semantic relationships among these concepts, often beyond what is directly observed in the data. To this end, we propose an energy minimization framework that leverages large-scale commonsense knowledge bases, such as ConceptNet, to provide contextual cues to establish semantic relationships among entities directly hypothesized from video. We mathematically express this using the language of Grenander's canonical pattern generator theory. We show that the use of prior encoded commonsense knowledge alleviate the need for large annotated training datasets and help tackle imbalance in training through prior knowledge. Using three different publicly available datasets - Charades, Microsoft Visual Description Corpus and Breakfast Actions datasets, we show that the proposed model can generate video interpretations whose quality is better than those reported by state-of-the-art approaches, which have substantial training needs. Through extensive experiments, we show that the use of commonsense knowledge from ConceptNet allows the proposed approach to handling various challenges such as training data imbalance, weak features and complex semantic relationships and visual scenes.
An enhanced high-order Boltzmann machine for feature engineering

Recently, automatic feature extraction and selection from unlabeled images that contain irrelevant patterns have been a proceeding interest. In this paper, an enhanced high-order Boltzmann machine is designed to promote the capacity of feature extraction and selection in a unified context. First, gating mechanism is employed for feature selection in comparison with conventional approaches. Then, two sets of hidden variables that the one set is real-valued latent variables and the other is spike latent variables are introduced to model the covariance structure of local patches, which can boost the abilities of feature learning and feature selection in turn. Simultaneously, the proposed model can infer in parallel via easy block Gibbs sampling without much training difficulty. Last, several extensions of the proposed model are developed to cope with different scenes. The massive performances obtained from various visual tasks have demonstrated that the proposed model can reach the highly improved performances over several currently excellent methods.
Guiding the selection of service identification methods based on SOA maturity level

Purpose A mature adoption of a service-oriented architecture (SOA) goes steadily through different levels of maturity, whereby each level has its own types of services (e.g. software services or business services). However, the identification of such services is not an easy task even though there exist many service identification methods (SIMs). This paper aims to propose a new approach to select SIMs. The proposed selection approach for SIMs uses the desired SOA maturity level as the main guidance to assist the organizations in selecting a suitable SIM for each level of SOA maturity. Design/methodology/approach The methodology consists of three activities: surveying and selecting a suitable evaluation framework for SIMs, surveying and selecting a suitable SOA maturity model (SOAMM) and using the selected evaluation framework to decide a suitable SIM for the desired SOA maturity level with respect to the selected SOAMM. Findings Welke’s SOAMM and two existing evaluation frameworks for SIMs were found suitable to validate the proposed selection approach for SIMs. The two selected frameworks utilized the proposed selection approach to different degrees. To fully utilize the proposed selection approach, a comprehensive evaluation framework is required that addresses the most significant aspects of the existing SIMs. Originality/value In this research, the authors propose a new way of using Welke’s SOAMM to guide the organizations in selecting a suitable SIM from the existing evaluation frameworks for SIMs based on the desired SOA maturity level. In addition, the proposed selection approach improves the applicability of the existing evaluation frameworks, as it provides the organizations with a new way to select the methods.
Capability for Multi-Core and Many-Core Memory Systems: A Case-Study With Xeon Processors

The general volume of data has exploded to unimaginable levels in the past decade. Therefore, the big data analytics has become an area of focus. Many frameworks have been developed for data analytics, such as Hadoop, Spark, etc. Most of the frameworks are built on multi-core or many-core memory systems, requiring developers and users to have an in-depth understanding of the architectures to take full advantage of the hardware. In this paper, we present a comprehensive study of both multi-core and many-core memory systems and discuss the different characteristics, including core, cache, memory, and the on-chip network. Furthermore, we propose a simple but effective mechanism for cache false-sharing overhead that can reduce a large number of LLC-load/store instructions and LLC cache-misses. In addition, we conduct detailed experiments with Ligra, a graph analytics framework, on four different-sized datasets. The results show that it can achieve up to a 2.5× and 9.5× speed-up for multi-core and many-core memory systems, respectively. We finally share our key findings and discuss the platform development on multi-core and many-core memory systems.
Space reduction constraints for the median of permutations problem

Given a set A⊆Sn of m permutations of {1,2,…,n} and a distance function d, the median problem consists of finding a permutation π∗ that is the “closest” of the m given permutations. Here, we study the problem under the Kendall-τ distance which counts the number of order disagreements between pairs of elements of two permutations. This problem has been proved to be NP-hard when m≥4, m even. In this article, which is a full version of the conference paper Milosz and Hamel (2016), we investigate new theoretical properties of A that solve the relative order between pairs of elements in median permutations of A, thus drastically reducing the search space of the problem. The resulting preprocessing of the problem is implemented with a Branch-and-Bound solving algorithm. We analyze its performance on randomly generated data and on real data.
Exemplar-based freckle retouching and skin tone adjustment

Abstract This paper presents a novel system for freckle retouching and skin tone adjustment based on a set of exemplar image pairs obtained before and after cosmetic laser therapy. Our key observation is that cosmetic laser therapy relies on decomposing and adjusting melanin and hemoglobin to change skin appearance, and the reflectance properties of the two pigments can greatly influence human skin color. We thus retouch freckles and adjust skin tone in a query image by changing the densities of melanin and hemoglobin to match the variation of those in the exemplar image pairs. Based on the proposed system, we can easily and intuitively generate an output image that looks similar to the results of cosmetic laser therapy. Our system also can detect more skin flaws and define their regions more accurately than previous retouching methods.