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Textual emotion detection is an attractive task while previous studies mainly focused on polarity or single-emotion classification. However, human expressions are complex, and multiple emotions often occur simultaneously with non-negligible emotion correlations. In this paper, a Multi-label Emotion Detection Architecture (MEDA) is proposed to detect all associated emotions expressed in a given piece of text. MEDA is mainly composed of two modules: Multi-Channel Emotion-Specified Feature Extractor (MC-ESFE) and Emotion Correlation Learner (ECorL). MEDA captures underlying emotion-specified features through MC-ESFE module in advance. MC-ESFE is composed of multiple channel-wise ESFE networks. Each channel is devoted to the feature extraction of a specified emotion from sentence-level to context-level through a hierarchical structure. Based on obtained features, emotion correlation learning is implemented through an emotion sequence predictor in ECorL. During model training, we define a new loss function, which is called multi-label focal loss. With this loss function, the model can focus more on misclassified positive-negative emotion pairs and improve the overall performance by balancing the prediction of positive and negative emotions. The evaluation of proposed MEDA architecture is carried out on emotional corpus: RenCECps and NLPCC2018 datasets. The experimental results indicate that the proposed method can achieve better performance than state-of-the-art methods in this task.

Key assignment and key maintenance in encrypted networks of resource-limited devices may be a challenging task, due to the permanent need of replacing out-of-service devices with new ones and to the consequent need of updating the key information. Recently, Aragona et al. proposed a new cryptographic scheme, ECTAKS, which provides a solution to this design problem by means of a Diffie-Hellman-like key establishment protocol based on elliptic curves and on a prime field. Even if the authors proved some results related to the security of the scheme, the latter still lacks a formal security analysis. In this paper, we address this issue by providing a security proof for ECTAKS in the setting of computational security, assuming that no adversary can solve the underlying discrete logarithm problems with non-negligible success probability.

A cost-effective stereolithography for medium-scale components is developed to fabricate 3D components with high build speed and resolution from photo-curable resin. The developed SLA utilizes a focused light beam of wavelength range (300 nm–700 nm) coming from the DLP projector and passes through the objective lens and finally imposed on the platform containing a layer of photo-curable resin. After focusing the light beam on the liquid resin layer, the photo-polymerization reaction occurs and the liquid resin becomes solid. Thus, the 3D object is fabricated layer by layer curing of liquid resin. The photopolymer used in this experimentation is polyethylene glycol di-acrylate and Irgacure 784 as photo-initiator. The Creo 3.0 software is used for modelling of 3D objects. A special MATLAB code is developed for slicing of the 3D model and displaying the sliced image one by one through DLP projector. The Arduino microcontroller with stepper motor and ball screw is used to control the motion of Z-stage platform. The Creation workshop software is also used to control motion of the Z-stage and period to display the sliced images through DLP projector. The medium-scale 3D objects with rectangular, square, and circular cross-sections are obtained by curing the aforementioned photo-curable resin. It is observed that the 3D objects are best cured for two seconds curing time with 0.1 mm curing depth along Z-axis.

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.

In this paper, we propose cryptanalytic results on two lightweight stream ciphers: Fountain v1 and Lizard. The main results of this paper are the followings: \begin{document}$ - $\end{document} We propose a zero-sum distinguisher on reduced round Fountain v1. In this context, we study the non-randomness of the cipher with a careful selection of cube variables. Our obtained cube provides a zero-sum on Fountain v1 till \begin{document}$ 188 $\end{document} initialization rounds and significant non-randomness till \begin{document}$ 189 $\end{document} rounds. This results in a distinguishing attack on Fountain v1 with \begin{document}$ 189 $\end{document} initialization rounds. \begin{document}$ - $\end{document} Further, we find that the same cipher has a weakness against conditional Time-Memory-Data-Tradeoff (TMDTO). We show that TMDTO attack using sampling resistance has online complexity \begin{document}$ 2^{110} $\end{document} and offline complexity \begin{document}$ 2^{146} $\end{document} . \begin{document}$ - $\end{document} Finally, we revisit the Time-Memory-Data-Tradeoff attack on Lizard by Maitra et al. (IEEE Transactions on Computers, 2018) and provide our observations on their work. We show that instead of choosing any random string, some particular strings would provide better results in their proposed attack technique.

Agriculture waste such as sugarcane bagasse (SCB) is composed mostly of cellulose, a very versatile natural biopolymer with well-established applications in many industries. The purpose of this study was to use combination treatment methods which include alkaline treatment and mild acid hydrolysis assisted with ultrasonication to extract cellulose nanofibers (CNFs) from sugarcane bagasse. Initially, the SCB fibers were treated with sodium hydroxide and aqueous hydrogen peroxide, a bleaching agent before subjected to mild acid hydrolysis followed by ultrasonication at 70% amplitude to defibrillate and disperse the fibers. The study focused on acid hydrolysis using mild sulfuric acid to the alkaline treated cellulose fibers extracted from SCB. The FESEM images of isolated CNFs exhibited diameter in the range 20–30 nm and length of up to several micrometers. This observation suggests that the combined treatment methods are highly effective in isolating CNFs from the plant biomass. Moreover, XRD analysis reveals the presence of peaks at 2θ of 15.2° and 22°, indicating cellulose type I with the crystallinity of 42%. Meanwhile, the FTIR spectra prove that individual CNF was successfully isolated due to the removal of non-cellulosic constituents. This result verifies that amorphous portions such as lignin and hemicellulose were completely removed. CNFs with long entangled network fibrils were successfully extracted from SCB through the combination of alkaline treatment and mild acid hydrolysis assisted with ultrasonication. The CNFs are expected to have high strength and aspect ratio that can be used as reinforced material in manufacturing nanocomposites.

This paper presents a practical optimum design solution of prismatic simply supported prestressed concrete girders that are subjected to uniformly distributed static loads. While assuming linear elastic behaviour and employing working stresses method, the optimum values of two design variables, namely the cross-sectional dimensions and the prestressing force, are formulated as a nonlinear optimization model. Optimum values of the design variables are computed to satisfy either a minimum weight or a minimum cost objective function. To solve the nonlinear programming models, a program named Non-Linear Programming Prestressed Concrete Optimizer (NLPRECO) was coded in MATLAB® programming environment. Some illustrative design problems were solved using NLPRECO and their results are demonstrated herein. Sensitivity analysis of the optimized design variables, to variations in design parameters, was also investigated and the results are presented. Results show substantial improvement over the regular un-optimized solutions and moderate improvement over a linear programming optimization solutions.

Nanoindentation derived mechanical properties and strain rate sensitivity assessment of a set of stir cast metallic reinforced Aluminium-based composites, with an antecedence of anomalous stress oscillations and strain rate insensitivity during hot compression, was investigated in this study. For the evaluation, Al6063 based composites reinforced with 6 wt% CuZnAl, steel, nickel, and SiC particles were subjected to nanoindentation, and their strain rate sensitivity was assessed using strain rate jump test. The results show that the AMCs reinforced with the metallic particles, presented better mechanical properties than those reinforced with SiC. The CuZnAl reinforced AMC had the best hardness (1.25 ± 0.25 GPa) and elastic modulus (∼83GPa), which is opined to be on account of thermoelastic contributions to the basic strengthening mechanisms. The large scatter observed in the mechanical response of the AMCs is largely due to the inhomogeneous particle distribution. The higher indentation resistance with an increase in strain rate, coupled with the absence of displacement bursts, indicated that the composites largely exhibit positive strain rate sensitivity.

The job sequencing and tool switching (SSP) is a well-known combinatorial optimization problem that aims to minimize the number of tool switches. The problem mainly arises in a flexible manufacturing system, in which machines can be configured with various tools to process different jobs and tool switches will correspond to a reduction of productivity. While the general studies assume uniform setup time for tool switching, applications on the industries indicate that the setup time of a tool might depend on the previously installed tool of the same slot. Therefore, this article proposes two integer linear programming models for the sequence-dependent SSP, namely the five-index formulation and the multicommodity flow formulation. Experimental tests are executed to compare the sequence-dependent SSP with the uniform SSP and the results show the effectiveness of multicommodity flow formulation. Finally, several managerial discussions are derived from the results.

Due to rapid movement of rural population to new regions and countries in order to find work and for better living conditions, population growth and economic development, there is gradual increase in production of waste and demand of electricity worldwide. According to World Bank statistics, the global Municipal Solid Waste generated was 1.3 billion tons in year 2012 and this is expected to increase above 2.2 billion tons per year by 2025. Due to lack of sites for waste disposal, it has become a serious environmental as well as health issue. Now various methods have been adopted for waste disposal. Among this, energy accessed from municipal solid waste is the most common practice adopted by developing countries. In addition, Waste to Energy and Energy from Waste technologies are used which include thermal and biological technologies. Gasification, pyrolysis and incineration are thermal technologies used in conversion of waste to energy. During these processes, syngas is produced which is used in combustion engine or turbines to generate electricity. Apart from this, biomethanation is also used in which biogas is produced. In this review article, we have discussed all the environmental and green methods used for the generation of energy from Municipal Solid Waste their advantages and drawbacks.

This study addresses performances and behaviors of the multi-cross-section HAWT blade design with and without fences. The FX66-S-196 V, FX63-137 S and SG6043, supercritical airfoils were used and distributed along the blade radius; further, the NACA4412 single-cross-section HAWT blade having the same dimensions was used to compare the behaviors and overall performances of all the blades. Analyses were then performed numerically using blade elements momentum BEM theory with a self-code (F.90) and CFD as well as experimentally with three multi-cross-section blades designed using SOLIDWORKS and 3D- printed with polylactic acid. the multi-cross-section HAWT blades show good performance compared with the single- cross-section blade, with an approximately 8% increase in power coefficient, i.e., 40 W, for a miniature wind turbine (127 cm diameter 500 W output power) and greater improvements for wind turbines with large diameters. The fences were designed using boundary layer theory and installed on multi-cross-section blades in experimentally determined positions. The fences showed high performance, with a 16% increase in total power coefficient and high flutter stability.

Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this paper, we investigated abnormal neural basis of conflict monitoring processes in MDD patients by applying dynamic causal modeling (DCM) technique on electroencephalography (EEG). The results indicated that MDD patients showed lower N2 amplitudes regardless of stimulus conditions, and reduced activation within ACC region for incongruent stimuli, relative to healthy controls. Especially, MDDs had more negative N2 amplitudes to happy incongruent trials than happy congruent trials. Source localization analyses revealed that MDD patients had significantly enhanced left inferior temporal gyrus (ITG) activation, which is involved in written words processing. Further DCM analysis provided abnormal neural correlates through greater backward connections (fusiform→ITG, amygdala→ITG) on happy incongruent trials than happy congruent trials in MDD group. These findings indicate that only sad words induce significantly greater interference effects to positive target faces in MDD patients, which may be associated with ITG activity dysfunction. The findings may share new insights into the neural mechanisms of emotional conflict processing in MDDs.

A generalized Stirling numbers of the second kind \begin{document}$ S_{a,b}\left(p,k\right) $\end{document} , involved in the expansion \begin{document}$ \left(an+b\right)^{p} = \sum_{k = 0}^{p}k!S_{a,b}\left(p,k\right) \binom{n}{k} $\end{document} , where \begin{document}$ a \neq 0, b $\end{document} are complex numbers, have studied in [ 16 ]. In this paper, we show that Bernoulli polynomials \begin{document}$ B_{p}(x) $\end{document} can be written in terms of the numbers \begin{document}$ S_{1,x}\left(p,k\right) $\end{document} , and then use the known results for \begin{document}$ S_{1,x}\left(p,k\right) $\end{document} to obtain several new explicit formulas, recurrences and generalized recurrences for Bernoulli numbers and polynomials.

This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of five primary body signals including gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and style. With the multiview image streams, we reconstruct high fidelity body expressions using 3D mesh models, which allows representing view-specific appearance. We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complementary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets. Based on HUMBI, we formulate a new benchmark challenge of a pose-guided appearance rendering task that aims to substantially extend photorealism in modeling diverse human expressions in 3D, which is the key enabling factor of authentic social tele-presence. HUMBI is publicly available at http://humbi-data.net, Comment: 18 pages; Accepted to TPAMI

In the context of digital signatures, the proxy signature holds a significant role of enabling an original signer to delegate its signing ability to another party (i.e., proxy signer). It has significant practical applications. Particularly it is useful in distributed systems, where delegation of authentication rights is quite common. For example, key sharing protocol, grid computing, and mobile communications. Currently, a large portion of existing proxy signature schemes are based on the hardness of problems like integer factoring, discrete logarithms, and/or elliptic curve discrete logarithms. However, with the rising of quantum computers, the problem of prime factorization and discrete logarithm will be solvable in polynomial-time, due to Shor's algorithm, which dilutes the security features of existing ElGamal, RSA, ECC, and the proxy signature schemes based on these problems. As a consequence, construction of secure and efficient post-quantum proxy signature becomes necessary. In this work, we develop a post-quantum proxy signature scheme Mult-proxy, relying on multivariate public key cryptography (MPKC), which is one of the most promising candidates of post-quantum cryptography. We employ a 5-pass identification protocol to design our proxy signature scheme. Our work attains the usual proxy criterion and a one-more-unforgeability criterion under the hardness of the Multivariate Quadratic polynomial (MQ) problem. It produces optimal size proxy signatures and optimal size proxy shares in the field of MPKC.

Regev (2005) introduced the learning with errors (LWE) problem and showed a quantum reduction from a worst case lattice problem to LWE. Building on the work of Peikert (2009), a classical reduction from the gap shortest vector problem to LWE was obtained by Brakerski et al. (2013). A concrete security analysis of Regev's reduction by Chatterjee et al. (2016) identified a huge tightness gap. The present work performs a concrete analysis of the tightness gap in the classical reduction of Brakerski et al. It turns out that the tightness gap in the Brakerski et al. classical reduction is even larger than the tightness gap in the quantum reduction of Regev. This casts doubts on the implication of the reduction to security assurance of practical cryptosystems.

In this paper, we study detectability problem of Networked Discrete Event Systems (NDES) where the communication between the plant and the agent is carried out through a network that can have several channels, so that, communication delays can cause changes in the order of the observations, and, also, loss of observations. We deploy an approach previously presented in the literature to construct an untimed nondeterministic automaton that models the behavior of a NDES, and, based on this model, we present a methodology for the state estimation of NDES, and new networked D-detectability definitions. Finally, we show that the NDES is networked D-detectable if, and only if, the equivalent untimed nondeterministic model is D-detectable.

Reports show that 40% of global carbon emissions come from energy consumed in buildings, so organizations around the world continuously seek ways to reduce their buildings energy consumption. Research identifies three main approaches; Retrofitting, the use of Low Carbon Buildings (LCB), and implementing Energy Management Systems (EMS). However, other factors could affect the success of these approaches; climate and local weather, building types, social acceptance, and implementation. This paper reviews and evaluates 50 reports on these implementations, and uses that evaluation to identify the most effective approach to reduce buildings carbon footprint. In this evaluation, authors used the reported success rate (in reducing carbon footprint) as key parameter, they then cross-checked this with origin of reports (country, climate region), building type, and implementation thoroughness. This allowed the authors to identify not only the best approach, but also understand the impact of these other factors on the reported success rate. Retrofitting was implemented the most (58%) but with varying success (2% to 49% reductions), this variation is attributed to the current state of the buildings and the level/method of retrofitting applied. LCB showed the highest reductions as energy saving measures were preplanned before building construction, this however limits LCB to new and planned buildings. Even though EMS showed results comparable to Retrofitting and required a fraction of the costs/effort, only 14% implemented it, this is an indication of the level of social awareness in the region on the need to reduce carbon footprint and its impact on the world’s climate.

In this paper, we first introduce a class of quartic Boolean functions. And then, the construction of weightwise perfectly balanced Boolean functions on \begin{document}$ 2^m $\end{document} variables are given by modifying the support of the quartic functions, where \begin{document}$ m $\end{document} is a positive integer. The algebraic degree, the weightwise nonlinearity, and the algebraic immunity of the newly constructed weightwise perfectly balanced functions are discussed at the end of this paper.

In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes which can perform well without channel state information. In this work, we present one such scheme, called wideband time frequency coding, which achieves rates on the order of the additive white Gaussian noise capacity without requiring any channel state information. Wideband time frequency coding combines impulsive frequency shift keying with pulse position modulation, which allows for information to be encoded in both the transmitted frequency and the transmission time period. On the detection side, we propose a non-coherent decoder based on a square-law detector, akin to the optimal decoder for frequency shift keying based signals. The impacts of various parameters on the symbol error probability and capacity of wideband time frequency coding are investigated, and the results show that it is robust to shadowing and highly fading channels. When compared to other modulation schemes such as code division multiple access, orthogonal frequency division multiplexing, pulse position modulation, and impulsive frequency shift keying without channel state information, wideband time frequency coding achieves higher rates in the wideband regime, and performs comparably in smaller bandwidths.

We prove the following theorem. Given a planar graph $G$ and an integer $k$, it is possible in polynomial time to randomly sample a subset $A$ of vertices of $G$ with the following properties: (i) $A$ induces a subgraph of $G$ of treewidth $\mathcal{O}(\sqrt{k}\log k)$, and (ii) for every connected subgraph $H$ of $G$ on at most $k$ vertices, the probability that $A$ covers the whole vertex set of $H$ is at least $(2^{\mathcal{O}(\sqrt{k}\log^2 k)}\cdot n^{\mathcal{O}(1)})^{-1}$, where $n$ is the number of vertices of $G$. Together with standard dynamic programming techniques for graphs of bounded treewidth, this result gives a versatile technique for obtaining (randomized) subexponential parameterized algorithms for problems on planar graphs, usually with running time bound $2^{\mathcal{O}(\sqrt{k} \log^2 k)} n^{\mathcal{O}(1)}$. The technique can be applied to problems expressible as searching for a small, connected pattern with a prescribed property in a large host graph, examples of such problems include Directed $k$-Path, Weighted $k$-Path, Vertex Cover Local Search, and Subgraph Isomorphism, among others. Up to this point, it was open whether these problems can be solved in subexponential parameterized time on planar graphs, because they are not amenable to the classic technique of bidimensionality. Furthermore, all our results hold in fact on any class of graphs that exclude a fixed apex graph as a minor, in particular on graphs embeddable in any fixed surface.

Oblivious RAM (ORAM), first introduced in the ground-breaking work of Goldreich and Ostrovsky (STOC ’87 and J. ACM ’96) is a technique for provably obfuscating programs’ access patterns, such that the access patterns leak no information about the programs’ secret inputs. To compile a general program to an oblivious counterpart, it is well-known that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varOmega (\log N)$$\end{document}Ω(logN) amortized blowup is necessary, where N is the size of the logical memory. This was shown in Goldreich and Ostrovksy’s original ORAM work for statistical security and in a somewhat restricted model (the so called balls-and-bins model), and recently by Larsen and Nielsen (CRYPTO ’18) for computational security. A long standing open question is whether there exists an optimal ORAM construction that matches the aforementioned logarithmic lower bounds (without making large memory word assumptions, and assuming a constant number of CPU registers). In this paper, we resolve this problem and present the first secure ORAM with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(\log N)$$\end{document}O(logN) amortized blowup, assuming one-way functions. Our result is inspired by and non-trivially improves on the recent beautiful work of Patel et al. (FOCS ’18) who gave a construction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(\log N\cdot \log \log N)$$\end{document}O(logN·loglogN) amortized blowup, assuming one-way functions. One of our building blocks of independent interest is a linear-time deterministic oblivious algorithm for tight compaction: Given an array of n elements where some elements are marked, we permute the elements in the array so that all marked elements end up in the front of the array. Our O(n) algorithm improves the previously best known deterministic or randomized algorithms whose running time is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n \cdot \log n)$$\end{document}O(n·logn) or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n \cdot \log \log n)$$\end{document}O(n·loglogn), respectively.

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation.

In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with massive true-label data from the source domain and unlabeled data from the target domain. However, collecting fully-true-label data in the source domain is high-cost and sometimes impossible. Compared to the true labels, a complementary label specifies a class that a pattern does not belong to, hence collecting complementary labels would be less laborious than collecting true labels. Thus, in this paper, we propose a novel setting that the source domain is composed of complementary-label data, and a theoretical bound for it is first proved. We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA). To this end, a complementary label adversarial network} (CLARINET) is proposed to solve CC-UDA and PC-UDA problems. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines on handwritten-digits-recognition and objects-recognition tasks., arXiv admin note: text overlap with arXiv:2007.14612

It is widely acknowledged that the success of deep learning is built on large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community.

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

Multi-set membership queries are fundamental operations in data science. In this paper, we propose a new data structure for multi-set membership queries, named coloring embedder, which is fast, accurate, and memory efficient. The idea of coloring embedder is to first map elements to a high-dimensional space, which nearly eliminates hashing collisions, and then use a dimensional reduction representation, similar to coloring a graph, to save memory. Theoretical proofs and experimental results show that the coloring embedder is effective in solving the problem of multi-set membership queries. We also find that web cache sharing is one of the typical application scenarios of the multi-set membership queries and current methods based on Bloom filters always send redundant queries. We apply coloring embedder to web cache sharing by arranging our data structure on the on-chip and off-chip memory and designing query, insertion and deletion operations for this scenario. The experimental results show that compared with the present method, our method can reduce the queries sent by proxies while reaching equal hit rate with the same size of on-chip memory. The source code of coloring embedder has been released on Github.

The most tedious and hectic job is to identify the tiny benthic animals by spending thousands of hour under the microscope, since all the fauna need to be counted, sorted, picked and permanently mounted on glass slides for taxonomic identification. All faunal identifications need a lot of preprocessing and it consumes a lot of time to identify a single specimen. Therefore, to reduce the complexity of many such procedures, combined with the desire to identify larger datasets, we came up with new software based on artificial intelligence which can automatically identify the benthic fauna through the microscopic images. In this paper, we propose a machine learning method for automatic visual identification through the images of the benthic fauna. To this end, we propose a neural network model, where we demonstrate that the proposed approach differentiates the fauna based on images. However, it works well with vast amounts of image data and significant computational resources.

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a dataset of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

The purpose of this study was to determine the strength of hollow concrete block masonry walls, beams, and columns, and compare their values with brick masonry walls and ordinary reinforced concrete beams and columns. The specimens for the hollow concrete block and brick masonry walls were prepared and tested for compressive and flexural strengths as well as horizontal and diagonal shear strengths. Moreover, two hollow concrete blocks and two ordinary reinforced concrete beams were designed to experience flexural and shear failure and loaded under a two-point bending load. Meanwhile, a hollow concrete block and an ordinary reinforced concrete column were also constructed and subjected to centric axial loads until they collapsed. The results showed the hollow concrete block masonry walls have greater strength than those made with bricks while both flexural and shear capacity as well as the flexural ductility of reinforced hollow concrete block beams were higher than ordinary reinforced concrete beams. Meanwhile, the centric compressive axial strength of the reinforced hollow concrete block column was smaller due to the inefficient bond it has with the infilled concrete which leads to their separation under the compressive axial load.

Initial wettability of rock surfaces plays a crucial role in displacement efficiency during core flooding experiments. In this study, linear alkylbenzene sulfonic acid (LABSA) and silica nanoparticles (NPs) were utilized as enhanced oil recovery (EOR) agents to improve oil recovery from carbonate rock samples. Prior to the core flooding experiments, effects of the presence of LABSA and SiO2 NPs on oil-water interfacial tension (IFT), wettability alteration, and surfactant adsorption on the rock surfaces were evaluated. The results of IFT/contact angle measurements showed that by adding 0.03 wt% LABSA, the IFT, and contact angle reduced from the initial values of 36.9 mN/m and 115.6° ± 0.2° to 8.3 mN/m and 100.3° ± 0.4°, respectively. Furthermore, incorporating SiO2 NPs (0.1 wt %) into the system causes a further decrease in IFT value (dropped to 2.2 mN/m), along with a substantial reduction in contact angle (final contact angle after 6 h soaking into the solution was measured as 64.8° ± 0.3°). In addition, surfactant loss due to the adsorption on the rock surfaces decreased up to 35% in the presence of SiO2 NPs (0.1 wt %). Moreover, various core flooding scenarios in carbonate plugs with different initial wettability conditions were conducted, and the performance of the EOR agents in enhancing oil recovery from oil-wet and water-wet core samples in the secondary and tertiary mode of flooding was evaluated. The outcomes revealed that the injection of a combination of chemicals, containing LABSA (0.03 wt %) and SiO2 (0.1 wt %) in the secondary mode leads to the highest ultimate oil recovery from sister carbonate core samples.

In this paper, a non‐negative matrix factorization feature expansion (NMFFE) approach was proposed to overcome the feature‐sparsity issue when expanding features of short‐text. First, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Second, we utilized the Dual regularization non‐negative matrix tri‐factorization (DNMTF) algorithm to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short‐text to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89%, and 1.79% on three data sets: Web snippets, Twitter sports, and AGnews, respectively compared with the Word2Vec algorithm and Char‐CNN algorithm. It indicated that the NMFFE algorithm was better than the BOW algorithm and the Char‐CNN algorithm in terms of classification accuracy and algorithm robustness.

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

Gum Ghatti (Anogeissus latifolia) is being widely used as an emulsifier, thickener, stabilizer in food, pharmaceutical, and allied industries due to its shelf life, tolerance of heat, and pH stability. Considering the oil & gas industry application, it is ideal for a hydraulic fracturing fluid additive as a direct replacement for Guar Gum. Basically, unlike Guar Gum, it contains less residual hull and it is suitable for low permeability unconventional reservoir; mainly shale gas reservoir, where permeability counts trivial in amount. The polymer of Ghatti aid exceptional rheological properties and help to produce higher molecular weight polymer; which has excellent proppant carrying capacity and fracture propagation. In this paper, the experimental study has been carried out in two different phases. This was achieved through optimization and characterization of hydraulic fracturing fluid which was embedded with gum matrices. In Phase-I, the study was carried out by using Response Surface Methodology (RSM). Wherein, the relation between several explanatory and response variables have been measured. In Phase-II, the characterization was done by using a Scanning Electron Microscope (SEM), Differential Scanning Calorimeter (DSC), Thermo-Gravimetric Analysis (TGA) and also, Fourier-Transform Infrared Spectroscopy (FT-IR). This experimental study will potentially benefit for development of a new hydraulic fracturing fluid. Where Gum Ghatti observed as a satisfactory alternative agent for Guar Gum.

Oil and gas industry continues to the increasing demand of more cost-effective well design and operations. Thus, team successfully eliminated intermediate casing string from the design thereby achieving not only a 33% reduction in dry hole cost/meter, but also delivering wells and producing oil 25% faster than before. Removing an intermediate casing string resulted in a longer open hole (more than 2000 mMD) which by default created new challenges which had to be dealt with and solved without compromising the targeted cost saving. The challenges can be summarized as follow: • Combining loss of circulation zone with unstable shale formations in one section. • PPFG uncertainties. • Bit selection and design optimization. • BHA selection and drive optimization. • Running wireline logging through a long open hole. • Running casing and having good quality cement. • Hole cleaning and displacement efficiency when changing mud systems. • Proper contingencies in place to meet the required completion strategy. Risk assessment, feasibility studies, workshops, and co-operation among all involved personnel has been conducted to ensure that this design optimization would result in positive outcome. Since the first implementation in 2019, more than 17 wells have been successfully delivered and completed in Best in Class performance based on latest update of Rushmore data tracking. The longest open hole section of 2790 m measured depth (MD) with total depth (TD) of 3910 mMD has been achieved generating above 800 Kusd saving per well, thus setting new benchmarks for the field and opening up new opportunities for the future. All this ensure Process Safety and compliance with Well Delivery process. This paper not only describes how two string design was successfully engineered and executed but also serves as a guide for selecting proper candidates for this design and an operational guide for two section wells design to ensure that these challenging long open hole will be successfully drilled while minimizing risks.

In plenty of real-life tasks, strongly supervised information is hard to obtain, such that there is not sufficient high-quality supervision to make traditional learning approaches succeed. Therefore, weakly supervised learning has drawn considerable attention recently. In this paper, we consider the problem of learning from incomplete and inaccurate supervision, where only a limited subset of training data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. We notice that in many applications, the limited labeled data are usually with one-sided noise. For instance, considering the bug detection task in the software system, the identified buggy codes are indeed with defects whereas the codes that have been checked many times or newly fixed may still have other flaws due to the complexity of the system. We propose a novel method which is able to effectively alleviate the negative influence of one-sided label noise with the help of a vast number of unlabeled data. Excess risk analysis is provided as theoretical justifications on the usefulness of incomplete and one-sided inaccurate supervision. We conduct experiments on synthetic, benchmark datasets, and real-life tasks to validate the effectiveness of the proposed approach.

This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

Meta-analysis is a statistical analysis of the data obtained from multiple studies and provides a quantitative synthesis of research results. It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known. However, due to the complexity of the environment and problems associated with the use of model in aquaculture, it remain few guidelines for the design, implementation or interpretation of meta-analysis in the field of aquaculture. Here, we first briefly reviewed the history of meta-analysis, then summarized the applications of meta-analysis in terms of major procedures, standards, and methods. Next, we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality. Overall, there is a large room for improving yield along the production chain. Large contributions can be found in breeding, feed, and farm management. For example, improving breeding can increase yield by 5.6% to 49%, depending on fish species and type of improvements. This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture. Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.

In the present paper, a neural network (NN) is proposed for detecting the human-manipulator collisions. Because of that purpose, three types of NNs are designed and trained named as; multilayer feedforward, cascaded forward, and recurrent NNs. The NN is designed based on the joints’ dynamics of the manipulator. In addition, the NN is trained using a dataset with and without the collisions using the algorithm of Levenberg-Marquardt to detect the collisions happened with the manipulator. During the design of the NN, only the intrinsic joint position sensor of the robot is used which enable the proposed method to be applied to any robot. The three designed neural networks are compared quantitatively and qualitatively to each other. Furthermore, the proposed method was evaluated with the KUKA LWR manipulator using one joint motion. The results of the designed systems achieve high effectiveness in detecting the collisions between human and robot.

A hybrid enhanced oil recovery (EOR) method by combining low salinity water (LSW) and low salinity surfactant (LSS) flooding techniques was designed. Different experiments were done to screen the Caspian seawater (SW) with altered ionic composition and surfactant, for the optimized performance in Kazakhstani carbonate oil fields. Changing to a more water-wet state and creating the middle phase were studied as the main criteria to select the best-engineered brine and anionic surfactant. The largest alteration towards the water-wet condition was recorded at 10 times dilution of the SW with 3- and 6- times spiked calcium and sulfate ions, respectively (10xSW-6SO4, Mg, 3Ca). This combination of anionic surfactants with carbonate formations is considered as a new approach in hybrid EOR methods. Among the anionic surfactants screened, Soloterra-113H (Alkyl Benzenesulfonic acid) showed the best solubilization ratio, aqueous stability, and Winsor type 3 microemulsions. The wettability alteration by the combination of optimized brine and screened surfactant was greater compared to the standalone LSW, which was confirmed by the 10° difference in contact angle measurement. The microemulsion phase constituted nearly 40% of the total height of the oil/brine column by the hybrid method. The recovery factor after injecting formation water was 52%, and it increased to 61% after optimized LSW injection. After switching to the engineered brine/surfactant, the recovery factor reached 70%, which proves the effectiveness of the hybrid method. The proposed combined method works better than either standalone EOR method due to the higher alteration in capillary number by changing wettability and reducing IFT, which leads to higher oil recovery.

We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). For this setting, we characterize the minimum total computation load and the minimum worst-case computation load in some cases, under the heterogeneous communication load constraints. While the total computation load depends on the sum of the computation loads of all the nodes, the worst-case computation load depends on the computation load of a node with the heaviest job. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and data shuffling. Beyond the cut-set bound, a novel converse is proposed using the proof by contradiction. For the general case, we identify two extreme regimes in which the scheme with coding and the scheme without coding are optimal, respectively.

A discontinuous conduction mode (DCM) high frequency high step up SEPIC-based converter with extended ZVS range is presented in this paper. With the incorporation of some components on conventional SEPIC, the proposed converter achieves better performances such as high voltage gain, low voltage stress and zero voltage switching (ZVS). The additional components form a resonant cell which enables the switches to work in ZVS and switching loss is reduced accordingly. In order to achieve ZVS, the converter is designed to work in DCM. Due to inclusion of the resonance stage, traditional PWM or PFM control method is unable to regulate the output characteristics and maintain ZVS of the power switches at the same time. Therefore, a hybrid control method is proposed in this paper to guarantee ZVS within a specific load range. According to this hybrid control method, the ZVS range is extended effectively. GaN switches and planar magnetic components are also used. Working principles and steady-state analysis are presented in detail. A 1MHz 36W prototype with 10 voltage gain has been designed to demonstrate theoretical analysis. Efficiency obtained is up to 91.8% at full load. Experimental results show good agreement with theoretical analysis.

In this paper, we study a novel service reliability augmentation problem in an Edge Computing network, where mobile users request network services with a service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant network function (VNF) instance placement in case their primary VNF instances fail. To this end, we first formulate a novel service reliability augmentation problem for each admitted request and show the NP-hardness of the problem. We then provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets, and then develop a randomized algorithm with a good approximation ratio and high probability for the service reliability augmentation problem, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We thirdly extend the proposed algorithm for a single request to the service reliability augmentation problem for a set of requests. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and superior to their analytical counterparts.

Classical data cleaning performs a minimal set of operations on the data to satisfy the given integrity constraints. Often, this minimization is equivalent to vertex cover, for example when tuples can be removed due to the violation of functional dependencies. Classically, the uncertainty of tuples and constraints is ignored. We propose not to view data as dirty but the uncertainty information about data. Since probabilities are often unavailable and their treatment is limited due to correlations in the data, we investigate a qualitative approach to uncertainty. Tuples are assigned degrees of possibility with which they occur, and constraints are assigned degrees of certainty that say to which tuples they apply. Our approach is non-invasive to the data as we lower the possibility degree of tuples as little as possible. The new resulting qualitative version of vertex cover remains NP-hard. We establish an algorithm that is fixed-parameter tractable in the size of the qualitative vertex cover. Experiments with synthetic and real-world data show that our algorithm outperforms the classical algorithm proportionally to the available number of uncertainty degrees. Based on the novel mining of the certainty degrees with which constraints hold, our framework becomes applicable even when uncertainty information is unavailable.

Drilling muds play an important role in drilling and production operations so that the degree of success in such operations is determined by the proper design of these fluids. To optimize drilling operations and avoid excessive expenditure, it is essential to prepare a drilling mud that is both cheap and efficient. Fluid additives are typically used in drilling systems to improve these fluids' capabilities in terms of hole cleaning capacity, filtration control, viscosity, yield point, heat transfer, etc. However, environmental concerns, cost of production, and availability are always considered challenging factors. This study introduces an eco-friendly herbal fiber in powder and regular forms as a potential alternative to other chemical additives. To do so, Alyssum homolocarpum seed mucilage was added with different concentrations to the water-based drilling muds, and its rheological behavior, filtration control, and the induced formation damage were examined and the optimum concentration was introduced. The results of this study revealed the advantages of the drilling muds formulated with Alyssum in terms of filtration control, rheology, formation damage, and greenness.

A mechanistic model has been applied for the prediction of corrosion rate in multiphase (water–oil-gas) flow regimes. This is due to inadequacies of existing models to adequately address a wide variety of corrosion dependent parameters with special emphasis on multiphase flow parameters. This model takes into account; water cut, volumetric phase fraction of oil and gas, temperature, pressure, other data collected relating to the pipe age, sand particle deposit, CO2 partial pressure, flow velocity, pH, chloride contents, mass transfer coefficients, and hydrogen sulfide. The applied model results show well above 91 percent prediction accuracy, while the prediction error ranges between 0.022 and 0.045 percent. When predicted results compared actual value (experimental data) with DeWaard and Milliams’ model; used in the oil and gas industries, it showed good agreement. It is seen that the resulting model produced an R2 value of 0.91 in comparison to 0.53, the value for Dewaard and Milliams’. It is obvious from numerical simulation that the multiphase flow parameters have major effects on the entire rate of corrosion of oil and gas multiphase flow pipelines. Finally, this research concluded by showing the developed model performance and comparison was made with numerical results. It is expected that the outcome of this research will be of significant help for the strengthening of the application of mechanistic models for predicting corrosion rate. This model will be useful for oil and gas industries worldwide for accurate prediction of corrosion rate.

Foam diversion acidizing can effectively solve the problem of acid distribution with severe heterogeneity between and within layers. Based on the foam diversion principle, the gas trap theory, and volume conservation principle, the foam slug diversion acidizing model was established and solved considering the change of bottomhole temperature and deviation factor of foam. The simulation results show that the change of temperature has a great influence on the diversion effect at the initial stage of injection, but a small influence at the middle and late stage. The effect of temperature on the highly permeable layer is greater than that of temperature on the low permeability layer. The deviation factor of foam is mainly controlled by temperature at the initial stage, and by pressure at the middle and late stage, and the whole process shows a downward trend, which has little influence on the diversion effect. The quasi-skin factor of gas trap is the most important parameter that influences the effect of foam diversion. The water saturation of the low permeability layer rises faster than that of the high permeability layer, and the effect of diversion is obvious. The research results have a strong guiding significance for foam diversion acidizing.

The drive to miniaturize power electronics motivates investigation into alternative passive component technologies such as piezoelectrics, which offer fundamentally higher power density and efficiency capabilities at small scales compared to magnetics. Piezoelectric transformers (PTs) have seen application in dc-dc converters, but these designs typically require additional magnetics for competitive efficiency. In this work, we systematically enumerate isolated and non-isolated dc-dc converter topologies and switching sequences capable of efficiently utilizing PTs as their only energy storage components. The proposed switching sequences maintain high-efficiency behaviors (e.g., zero voltage switching (ZVS), all-positive instantaneous power transfer, and minimal charge circulation) across wide voltage gain and load ranges. We present techniques for deriving these switching sequences’ ZVS regions, estimating PT efficiency, and solving for periodic steady state switching times; these offer insights for comparing and implementing design options. We then verify our analyses in a 180-500 V experimental prototype based on a commercially-available PT, which demonstrates significant efficiency gain through a proposed implementation.