Reviewerly/exHarmony huggingface dataset:
Online Adaptive Supervised Hashing for Large-Scale Cross-Modal Retrieval

In recent years, with the continuous growth of multimedia data on the Internet, multimodal hashing has attracted increasing attention for its efficiency in large-scale cross-modal retrieval. Typically, most existing multimodal hashing methods are batch-based methods that cannot deal with the growing streaming data. Online multimodal hashing adopts online learning strategy to learn hash models incrementally, which can process large-scale streaming data. However, existing supervised online multimodal hashing methods still suffer from several limitations: (1) most methods cannot update hash codes of old data when hash model changes, which will hinder the retrieval accuracy. (2) the discrete optimizations of most methods for learning binary hash codes are less efficient or less effective. To address the above limitations, an efficient supervised online multimodal hashing method termed Online Adaptive Supervised Hashing (OASH) is proposed in this paper. OASH regresses class labels and training data to binary hash codes to learn discrete hash codes and hash functions with an efficient online optimization scheme. It learns hash functions incrementally with newly arriving data and updates hash codes with the latest hash model. By adaptively updating hash functions and hash codes with new data rather than accessing to old data, it gains much performance enhancement and computation savings. Extensive experiments on three benchmark data sets validate the superiority of OASH over state-of-the-art methods in both accuracy and efficiency.
Simultaneous transmission of classical and quantum information under channel uncertainty and jamming attacks

We derive universal codes for simultaneous transmission of classical messages and entanglement through quantum channels, possibly under attack of a malignant third party. These codes are robust to different kinds of channel uncertainty. To construct such universal codes, we invoke and generalize properties of random codes for classical and quantum message transmission through quantum channels. We show these codes to be optimal by giving a multi-letter characterization of regions corresponding to the capacity of compound quantum channels for simultaneously transmitting and generating entanglement with classical messages. Also, we give dichotomy statements in which we characterize the capacity of arbitrarily varying quantum channels for simultaneous transmission of classical messages and entanglement. These include cases where the malignant jammer present in the arbitrarily varying channel model is classical (chooses channel states of product form) and fully quantum (is capable of general attacks not necessarily of product form).
Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.First, the self-attention mechanism is added to convolutional matrix factorization and a probabilistic matrix factorization model, based on a convolutional neural networks with self-attention mechanism, is proposed.Second, after integrating auxiliary information, such as item comment, item name, and item category, probabilistic matrix factorization, based on a self-attention mechanism convolutional neural networks, is used for recommendation.Adding the self-attention mechanism allows convolutional matrix factorization to capture the long-distance dependence between different components of the auxiliary information.Integrating the item comment, name, and category information alleviates the data sparsity of recommendation, and improves the accuracy of rating prediction.Experimental results on the MovieLens-1M and MovieLens-10M datasets show that the probabilistic matrix factorization recommendation of selfattention mechanism convolutional neural networks with item comment, name, and category information is superior to existing popular methods, in respect of root mean square error.
Research and Implementation of Chinese Chess Game Algorithm Based on Reinforcement Learning

Chinese chess is a two-person zero-sum game with complete knowledge like the widely used game Go and Chess. This paper designs a Chinese chess game algorithm based on reinforcement learning. It adopts a self-play learning model constructed by combining deep convolutional neural networks and Monte Carlo search tree algorithm, and uses Monte Carlo search tree algorithm to simulate chess moves that constantly change roles. The deep neural network improves the chess strategy, and trains the deep neural network in the reverse direction after winning or losing, so as to try to obtain a certain level of chess skill in Chinese chess through self-learning without initial training data. After a period of training, through the design and the traditional alpha-beta pruning algorithm and Monte Carlo algorithm comparison and its own competition analysis and other experiments, it is found that the model has strong self-learning ability, and has a good performance in Chinese chess. There is more room for improvement in chess power.
Towards a Broader Acceptance of Formal Verification Tools

Formal methods face challenges towards wide acceptance and adoption in software development practices. The major reason is presumed complexity of the concepts, tools and formal processes. The issue can be addressed by academia with a thoughtful plan of teaching and practise. The user study detailed in this paper is examining AutoProof tool with the motivation to identify complexities attributed to formal methods. Participants’ (students of Masters program in Computer Science) performance and feedback on the experience with formal methods assisted us in extracting specific problem areas that effect tool usability. The study results infer, along with improvements in verification tool functionalities, that teaching programs need to be modified by including pre-requisite courses to make formal methods easily adapted by students and promote their usage in software development process.
Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records

Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the results for complex and big EHR data. We used a system entailing sequence mining, interpretable deep learning models, and visualization on data extracted from the MIMIC-IIIdatabase for a group of patients diagnosed with heart disease. The results of sequence mining corresponded to specific pathways of interest to medical staff and were used to select patient groups that underwent these pathways. An interactive Sankey diagram representing these pathways and a heat map visually representing the weight of each variable were developed for temporal and quantitative illustration. We applied the proposed system to predict unplanned cardiac surgery using clinical pathways determined by sequence pattern mining to select cardiac surgery from complex EHRs to label subject groups and deep learning models. The proposed system aids in the selection of pathway-based patient groups, simplification of labeling, and exploratory the interpretation of the modeling results. The proposed system can help medical staff explore various pathways that patients have undergone and further facilitate the testing of various clinical hypotheses using big data in the medical domain.
Developing a Curated Topic Model for COVID-19 Medical Research Literature

Topic models can facilitate search, navigation, and knowledge discovery in large document collections.However, automatic generation of topic models can produce results that fail to meet the needs of users.We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
Experimental investigation of the uncertainty relations with coherent light

Taking advantage of coherent light beams, we experimentally investigate the variancebased uncertainty relations and the optimal majorization uncertainty relation for the two-dimensional quantum mechanical system.Different from most of the experiments which devoted to record each individual quantum, we examine the uncertainty relations by measuring an ensemble of photons with two polarization degree of freedom characterized by the Stokes parameters which allow us to determine the polarization density matrix with high precision. The optimality of the recently proposed direct-sum majorization uncertainty relation is verified by measuring the Lorenz curves. Results show that the Lorenz curve method represents a faithful verification of the majorization uncertainty relation and the uncertainty relation is indeed an ensemble property of quantum system.
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter

Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters. It uses a particle filter (PF) at each iteration of an MCMC algorithm to unbiasedly estimate the likelihood for a given static parameter value. However, pMCMC can be computationally intensive when a large number of particles in the PF is required, such as when the data are highly informative, the model is misspecified and/or the time series is long. In this paper we exploit the ensemble Kalman filter (EnKF) developed in the data assimilation literature to speed up pMCMC. We replace the unbiased PF likelihood with the biased EnKF likelihood estimate within MCMC to sample over the space of the static parameter. On a wide class of different non-linear SSM models, we demonstrate that our extended ensemble MCMC (eMCMC) methods can significantly reduce the computational cost whilst maintaining reasonable accuracy. We also propose several extensions of the vanilla eMCMC algorithm to further improve computational efficiency. Computer code to implement our methods on all the examples can be downloaded from https://github.com/cdrovandi/Ensemble-MCMC.
Coordinated Priority-aware Charging of Distributed Batteries in Oversubscribed Data Centers

Data centers employ batteries for uninterruptible operation during maintenance and power failures, for example, when switching to diesel generator power after a utility power failure. Depleted batteries start to recharge once the input power is back, creating a sudden power spike in the power hierarchy. If not properly controlled, a sustained power overload can potentially trip circuit breakers, leading to service outages. Power overloads due to battery recharging are even more likely in oversubscribed data centers where the power infrastructure is aggressively provisioned for high utilization. The problem caused by simultaneous recharging of batteries in a data center has not been extensively studied and no real-world solutions have been proposed in the literature.In this paper, we identify the problem due to battery recharging with case studies from Facebook's data centers. We describe the solutions we have developed to coordinate charging of batteries without exceeding the circuit breaker power limit. We explain in detail, the variable battery charging algorithm built into the distributed battery charger hardware deployed in Facebook data centers, and the system design considerations necessary on a large scale. The new variable charger is able to reduce battery recharge power by up to 80%. We further leverage individual battery charging control mechanism to coordinate the charging process such that we charge the batteries according to priorities of applications running on the servers supported by the batteries. We evaluate our coordinated priority-aware battery charging algorithm by building a prototype in a Facebook production data center as well as through simulation experiments using production power traces. Our results show that we are able to meet reliability service level agreements by using our battery recharging algorithm, while satisfying given power constraints.
Dynamics of Quantum Correlations in a Qubit-Oscillator System Interacting via a Dissipative Bath

The entanglement dynamics in a bipartite system consisting of a qubit and a harmonic oscillator interacting only through their coupling with the same bath is studied. The considered model assumes that the qubit is coupled to the bath via the Jaynes-Cummings interaction, whilst the position of the oscillator is coupled to the position of the bath via a dipole interaction. We give a microscopic derivation of the Gorini-Kossakowski-Sudarshan-Lindblad equation for the considered model. Based on the Kossakowski Matrix, we show that non-classical correlations including entanglement can be generated by the considered dynamics. We then analytically identify specific initial states for which entanglement is generated. This result is also supported by our numerical simulations.
An online gradient-based parameter identification algorithm for the neuro-fuzzy systems

Online gradient descent method has been widely applied for parameter learning in neuro-fuzzy systems. The success of the application relies on the convergence of the learning procedure. However, there barely have been convergence analyses on the online learning procedure for neuro-fuzzy systems. In this paper, an online gradient learning algorithm with adaptive learning rate is proposed to identify the parameters of the neuro-fuzzy systems representing the Mamdani fuzzy model with Gaussian fuzzy sets. We take the reciprocals of the variances of the Gaussian membership functions, rather than the variances themselves, as independent variables when computing the gradient with respect to the variance parameters. Subsequently, oscillation of the gradient value in the learning process can be avoided. Furthermore, some convergence results for this online learning scheme are studied. Finally, three numerical examples are provided to illustrate the performance of the proposed algorithm.
Modeling and Analysis of Hadoop MapReduce Systems for Big Data Using Petri Nets

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Majorization and the time complexity of linear optical networks

This work shows that the majorization of photon distributions is related to the runtime of classically simulating multimode passive linear optics, which explains one aspect of the boson sampling hardness. A Shur-concave quantity which we name the \emph{Boltzmann entropy of elementary quantum complexity} ($S_B^q$) is introduced to present some quantitative analysis of the relation between the majorization and the classical runtime for simulating linear optics. We compare $S_B^q$ with two quantities that are important criteria for understanding the computational cost of the photon scattering process, $\mathcal{T}$ (the runtime for the classical simulation of linear optics) and $\mathcal{E}$ (the additive error bound for an approximated amplitude estimator). First, for all the known algorithms for computing the permanents of matrices with repeated rows and columns, the runtime $\mathcal{T}$ becomes shorter as the input and output distribution vectors are more majorized. Second, the error bound $\mathcal{E}$ decreases as the majorization difference of input and output states increases. We expect that our current results would help in understanding the feature of linear optical networks from the perspective of quantum computation.
Unitary circuit synthesis for tomography of generalized coherent states

We present a method that outputs a sequence of simple unitary operations to prepare a given quantum state that is a generalized coherent state. Our method takes as inputs the expectation values of some relevant observables on the state to be prepared. Such expectation values can be estimated by performing projective measurements on $O(M^3 \log(M/\delta)/\epsilon^2)$ copies of the state, where $M$ is the dimension of an associated Lie algebra, $\epsilon$ is a precision parameter, and $1-\delta$ is the required confidence level. The method can be implemented on a classical computer and runs in time $O(M^4 \log(M/\epsilon))$. It provides $O(M \log(M/\epsilon))$ simple unitaries that form the sequence. The number of all computational resources is then polynomial in $M$, making the whole procedure very efficient in those cases where $M$ is significantly smaller than the Hilbert space dimension. When the algebra of relevant observables is determined by some Pauli matrices, each simple unitary may be easily decomposed into two-qubit gates. We discuss applications to quantum state tomography and classical simulations of quantum circuits.
A privacy-preserving consensus mechanism for an electric vehicle charging scheme

The increasing adoption of Electric Vehicles (EVs) brings opportunities for electric power transactions for users to be able to trade their excess electricity. Blockchain technology is envisaged as an electricity exchange platform to minimise the time gap and cost in the trading process. We propose a public electric power exchange service network with a gateway-based privacy-preserving scheme to implement authentication and data encryption for EV charging and discharging from grid peers. A novel consensus mechanism Proof-of-Benefit (PoB) is designed to support automated smart contracts for electric power trading, in which a benefit number generation algorithm is proposed to select the leader for the transaction validation. Meanwhile, the authentication strategy ensures only the participated nodes with valid identities can access the consensus process, and the data encryption is applied to avoid EV behaviour analysis based upon plain transaction flows that would otherwise be exposed in the network. Through theoretical and experimental analysis, a public electric power exchange service platform with a PoB consensus mechanism achieves a higher scalability and lower grid load variance while maintaining a high accuracy rate of correct electric power transactions against Sybil attacks and a faster (lower time consumption) authentication process when compared with other blockchain-enabled authentication methods (e.g. FairAccess, Bubbles of trust, and so on) used as the baseline.
Differential Scene Flow from Light Field Gradients

This paper presents novel techniques for recovering 3D dense scene flow, based on differential analysis of 4D light fields. The key enabling result is a per-ray linear equation, called the ray flow equation, that relates 3D scene flow to 4D light field gradients. The ray flow equation is invariant to 3D scene structure and applicable to a general class of scenes, but is under-constrained (3 unknowns per equation). Thus, additional constraints must be imposed to recover motion. We develop two families of scene flow algorithms by leveraging the structural similarity between ray flow and optical flow equations: local 'Lucas-Kanade' ray flow and global 'Horn-Schunck' ray flow, inspired by corresponding optical flow methods. We also develop a combined local-global method by utilizing the correspondence structure in the light fields. We demonstrate high precision 3D scene flow recovery for a wide range of scenarios, including rotation and non-rigid motion. We analyze the theoretical and practical performance limits of the proposed techniques via the light field structure tensor, a 3x3 matrix that encodes the local structure of light fields. We envision that the proposed analysis and algorithms will lead to design of future light-field cameras that are optimized for motion sensing, in addition to depth sensing.
Integrating Educational Technology in Mathematics Education in Economically Disadvantaged Areas in South Africa

This qualitative case study adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) created by Venkatesh, Morris, Davis, and Davis (2003 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi:10.2307/30036540. doi:10.2307/30036540[Crossref], [Web of Science ®] , [Google Scholar]) to investigate the elements that facilitate and hinder the integration of educational technology in mathematics education in economically disadvantaged areas of South Africa. Semi-structured interviews were conducted with two school leaders and three Grade 5 mathematics teachers from two smart schools, one in Gauteng and the other in the Western Cape Province, which were conveniently and purposefully selected. Additionally, one mathematics lesson was observed at each of these schools. To analyze the responses from interviews, content analyses were used. Findings showed that facilitating conditions such as adequate technological infrastructure and qualified information technology technicians influenced the actual teacher use of educational technology in mathematics instruction. However, social influence had the largest impact on these teachers’ integration of educational technology in mathematics education. Implications for practice are discussed.
Efficient Rule Generation for Associative Classification

Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).