Reviewerly/exHarmony huggingface dataset:
Mobile library initiatives: a new way to revitalize the academic library settings

Purpose The purpose of this paper is to understand the mobile pervasiveness among different categories of student’s gender-wise vis-a-vis to investigate user perception to access library content in innovative ways. The study tries to understand the potential demand of some features in mobile library initiatives. In addition, this study aims to determine willingness and need of mobile library services. The information gained from the study is intended to help the libraries to realize the growing demand of mobile library services. Design/methodology/approach A questionnaire was designed to gather feedback on student’s perception regarding the important mobile service features which they find useful to be included in different library initiatives. The aim was to understand the pervasiveness of mobile devices and information needs in Indian Academia. Moreover, which features can be added to new initiatives so as to deliver robust services to the users in their comfort zones. Findings The responses received indicate that a significant number of students are ready to adopt mobile library initiatives with the desired features in them, if provided to them by their respective libraries. The results of the study have provided the necessary information on what the users really want. It is therefore incumbent upon the universities/institutions of higher education to start to work with the Web-based library services and move it into mobile library service. Research limitations/implications The findings of this study provide a benchmark of mobile library initiatives with the choicest features based on the user perception. The data collected may also give an indication about compelling services that could help the users to access information ubiquitously. Originality/value Libraries can better serve their patrons by understanding the growing capabilities of mobile devices; it is incumbent upon libraries to mobilize their services by embracing mobile library initiatives. Moreover, to realize the needs of users and provide only such services which are more in demand.
Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting

ABSTRACT Estimating the parameters of a model describing a set of observations using a neural network is, in general, solved in a supervised way. In cases when we do not have access to the model’s true parameters, this approach can not be applied. Standard unsupervised learning techniques, on the other hand, do not produce meaningful or semantic representations that can be associated with the model’s parameters. Here we introduce a novel self-supervised hybrid network architecture that combines traditional neural network elements with analytic or numerical models, which represent a physical process to be learned by the system. Self-supervised learning is achieved by generating an internal representation equivalent to the parameters of the physical model. This semantic representation is used to evaluate the model and compare it to the input data during training. The semantic autoencoder architecture described here shares the robustness of neural networks while including an explicit model of the data, learns in an unsupervised way, and estimates, by construction, parameters with direct physical interpretation. As an illustrative application, we perform unsupervised learning for 2D model fitting of exponential light profiles and evaluate the performance of the network as a function of network size and noise.
Quantum bath statistics tagging

The possibility of discriminating the statistics of a thermal bath using indirect measurements performed on quantum probes is presented. The scheme relies on the fact that, when weakly coupled with the environment of interest, the transient evolution of the probe toward its final thermal configuration, is strongly affected by the fermionic or bosonic nature of the bath excitations. Using figures of merit taken from quantum metrology such as the Holevo-Helstrom probability of error and the Quantum Chernoff bound, we discuss how to achieve the greatest precision in this statistics tagging procedure, analyzing different models of probes and different initial preparations and by optimizing over the time of exposure of the probe.
Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings

Stretched words like 'heellllp' or 'heyyyyy' are a regular feature of spoken language, often used to emphasize or exaggerate the underlying meaning of the root word. While stretched words are rarely found in formal written language and dictionaries, they are prevalent within social media. In this paper, we examine the frequency distributions of 'stretchable words' found in roughly 100 billion tweets authored over an 8 year period. We introduce two central parameters, 'balance' and 'stretch', that capture their main characteristics, and explore their dynamics by creating visual tools we call 'balance plots' and 'spelling trees'. We discuss how the tools and methods we develop here could be used to study the statistical patterns of mistypings and misspellings and be used as a basis for other linguistic research involving stretchable words, along with the potential applications in augmenting dictionaries, improving language processing, and in any area where sequence construction matters, such as genetics.
Towards a Verified Model of the Algorand Consensus Protocol in Coq

The Algorand blockchain is a secure and decentralized public ledger based on pure proof of stake rather than proof of work. At its core it is a novel consensus protocol with exactly one block certified in each round: that is, the protocol guarantees that the blockchain does not fork. In this paper, we report on our effort to model and formally verify the Algorand consensus protocol in the Coq proof assistant. Similar to previous consensus protocol verification efforts, we model the protocol as a state transition system and reason over reachable global states. However, in contrast to previous work, our model explicitly incorporates timing issues (e.g., timeouts and network delays) and adversarial actions, reflecting a more realistic environment faced by a public blockchain. Thus far, we have proved asynchronous safety of the protocol: two different blocks cannot be certified in the same round, even when the adversary has complete control of message delivery in the network. We believe that our model is sufficiently general and other relevant properties of the protocol such as liveness can be proved for the same model.
Infinite Task Learning in RKHSs

Machine learning has witnessed tremendous success in solving tasks depending ona single hyperparameter. When considering simultaneously a finite number oftasks, multi-task learning enables one to account for the similarities of the tasksvia appropriate regularizers. A step further consists of learning a continuumof tasks for various loss functions. A promising approach, calledParametric Task Learning, has paved the way in the continuum setting for affine models and piecewise-linear loss functions. In this work, we introduce a novelapproach called Infinite Task Learning whose goal is to learn a function whose output is a function over the hyperparameter space. We leverage tools fromoperator-valued kernels and the associated vector-valued RKHSs that provide anexplicit control over the role of the hyperparameters, and also allows us toconsider new type of constraints. We provide generalization guarantees to thesuggested scheme and illustrate its efficiency in cost-sensitiveclassification, quantile regression and density level set estimation.
Loc2Vec-Based Cluster-Level Transition Behavior Mining for Successive POI Recommendation

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help to suggest new locations and makes LBSNs more prevalent to users. Successive POI recommendation is a nature extension of the general POI recommendation which utilizes users’ current state like the latest check-in records or timestamps to recommend subsequent POIs. Successive POI recommendation requires a well-constructed model for the transition patterns in POI sequences; however, existing works still have some limitations: 1) transitions are modeled on a relatively low level which cannot reflect users’ real intentions hidden behind; 2) there lacks a balance between the transition patterns modeled globally and personally; and 3) most works only consider the correlations between adjacent check-ins, but longer dependencies should be captured as well. To resolve the above issues, we present a successive POI recommendation approach called TTR which is based on the personalized transition pattern analysis on the cluster level for the check-in data in LBSN. It first clusters the POIs based on their representation vectors learnt from Word2Vec model, then it models users’ transition behavior on the cluster level through the additive Markov chain model, finally it recommends successive POIs based on a combination of personalized and global strategy. We conduct several experiments on the real datasets Gowalla and Brightkite to evaluate the performance of TTR, and the results show that the proposed method outperforms existing works in terms of precision and recall metrics, and the personalized strategy shows better performance while the global strategy can provide better diversity.
LSTM-Based with Deterministic Negative Sampling for API Suggestion

Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&amp;A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency.
Can a Machine Learn from Radiologists’ Visual Search Behaviour and Their Interpretation of Mammograms—a Deep-Learning Study

Visual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists’ attentional level and the interpretation of mammograms. We seek to determine whether these models are practical and feasible for use in training and teaching programmes. Eight radiologists of varying experience levels in reading mammograms reviewed 120 two-view digital mammography cases (59 cancers). Their search behaviour and decisions were captured using a head-mounted eye-tracking device and software allowing them to record their decisions. This information from radiologists was used to build an ensembled machine-learning model using top-down hierarchical deep convolution neural network. Separately, a model to determine type of missed cancer (search, perception or decision-making) was also built. Analysis and comparison of variants of these models using different convolution networks with and without transfer learning were also performed. Our ensembled deep-learning network architecture can be trained to learn about radiologists’ attentional level and decisions. High accuracy (95%, p value ≅ 0 [better than dumb/random model]) and high agreement between true and predicted values (kappa = 0.83) in such modelling can be achieved. Transfer learning techniques improve by < 10% with the performance of this model. We also show that spatial convolution neural networks are insufficient in determining the type of missed cancers. Ensembled hierarchical deep convolution machine-learning models are plausible in modelling radiologists’ attentional level and their interpretation of mammograms. However, deep convolution networks fail to characterise the type of false-negative decisions.
Randomized benchmarking in the analogue setting

Abstract Current development in programmable analogue quantum simulators (AQS), whose physical implementation can be realised in the near-term compared to those of large-scale digital quantum computers, highlights the need for robust testing techniques in analogue platforms. Methods to properly certify or benchmark AQS should be efficiently scalable, and also provide a way to deal with errors from state preparation and measurement (SPAM). Up to now, attempts to address this combination of requirements have generally relied on model-specific properties. We put forward a new approach, applying a well-known digital noise characterisation technique called randomized benchmarking (RB) to the analogue setting. RB is a scalable experimental technique that provides a measure of the average error-rate of a gate-set on a quantum hardware, incorporating SPAM errors. We present the original form of digital RB, the necessary alterations to translate it to the analogue setting and introduce the analogue randomized benchmarking protocol (ARB). In ARB we measure the average error-rate per time evolution of a family of Hamiltonians and we illustrate this protocol with two case-studies of analogue models; classically simulating the system by incorporating several physically motivated noise scenarios. We find that for the noise models tested, the data fit with the theoretical predictions and we gain values for the average error rate for differing unitary sets. We compare our protocol with other relevant RB methods, where both advantages (physically motivated unitaries) and disadvantages (difficulty in reversing the time-evolution) are discussed.
On maximum-likelihood decoding with circuit-level errors

Error probability distribution associated with a given Clifford measurement circuit is described exactly in terms of the circuit error-equivalence group, or the circuit subsystem code previously introduced by Bacon, Flammia, Harrow, and Shi. This gives a prescription for maximum-likelihood decoding with a given measurement circuit. Marginal distributions for subsets of circuit errors are also analyzed; these generate a family of related asymmetric LDPC codes of varying degeneracy. More generally, such a family is associated with any quantum code. Implications for decoding highly-degenerate quantum codes are discussed.
Genetic algorithm rule based categorization method for textual data mining

The rule based categorization approaches such as associative classification have the capability to produce classifiers rival to those learned by traditional categorization approaches such as Naïve Bayes and K-nearest Neighbor.However, the lack of useful discovery and usage of categorization rules are the major challenges of rule based approaches and their performance is declined with large set of rules.Genetic Algorithm (GA) is effective to reduce the high dimensionality and improve categorization performance.However, the usage of GA in most researches is limited in the categorization preprocessing stage and its results is used to simplify the categorization process performed by other categorization algorithms.This paper proposed a hybrid GA rule based categorization method, named genetic algorithm rule based categorization (GARC), to enhance the accuracy of categorization rules and to produce accurate classifier for text mining.The GARC consists of three main stages; namely, search space determination, rule discovery with validation (rule generation), and categorization.The experimental results are carried out on three Arabic text datasets with multiple categories to evaluate the efficiency of GARC.The results show that a promising performance was achieved by using GARC for Arabic text categorization.The GARC achieves the best performance with small feature space in most situations..
Generalized Lehmer Mean for Success History based Adaptive Differential Evolution

The Differential Evolution (DE) is a highly competitive numerical optimization algorithm, with a small number of control parameters. However, it is highly sensitive to the setting of these parameters, which inspired many researchers to develop adaptation strategies. One of them is the popular Success-History based Adaptation (SHA) mechanism, which significantly improves the DE performance. In this study, the focus is on the choice of the metaparameters of the SHA, namely the settings of the Lehmer mean coefficients for scaling factor and crossover rate memory cells update. The experiments are performed on the LSHADE algorithm and the Congress on Evolutionary Computation competition on numerical optimization functions set. The results demonstrate that for larger dimensions the SHA mechanism with modified Lehmer mean allows a significant improvement of the algorithm efficiency. The theoretical considerations of the generalized Lehmer mean could be also applied to other adaptive mechanisms.
Graph Laplacian Regularization for Robust Optical Flow Estimation

This paper proposes graph Laplacian regularization for robust estimation of optical flow. First, we analyze the spectral properties of dense graph Laplacians and show that dense graphs achieve a better trade-off between preserving flow discontinuities and filtering noise, compared with the usual Laplacian. Using this analysis, we then propose a robust optical flow estimation method based on Gaussian graph Laplacians. We revisit the framework of iteratively reweighted least-squares from the perspective of graph edge reweighting, and employ the Welsch loss function to preserve flow discontinuities and handle occlusions. Our experiments using the Middlebury and MPI-Sintel optical flow datasets demonstrate the robustness and the efficiency of our proposed approach.
Recovering the full dimensionality of hyperentanglement in collinear photon pairs

Exploiting the simultaneous entanglement (hyperentanglement) in multiple degrees of freedom of an entangled photon pair increases the dimensionality of the Hilbert space for quantum information processing. However, the generation of hyperentangled photon pairs collinearly, while capable of high brightness, results in a smaller Hilbert space due to the two photons being in the same spatial mode. In this paper, we show that one can recover the full dimensionality of such hyperentanglement through a simple interference setup, similar to the time-reversed Hong-Ou-Mandel (TR-HOM) process. In contrast to the standard TR-HOM, we point out that a critical phase condition has to be satisfied in order to recover the hyperentanglement. We theoretically analyze the realization of this approach and discuss the feasibility of generating truly hyperentangled photon pairs. Our proposed approach does not require postselection and hence enables efficient hyperentangled photon pair generation for high-dimensional quantum applications.
Using User Contextual Profile for Recommendation in Collaborations

Nowadays, many digital technologies are developed to support collaboration and facilitate its efficiency. When using them, users will leave contextual information explicitly and implicitly, which could contribute to identifying users’ situations and thus enabling systems to generate corresponding recommendations. In the framework of collaborations, we are interested in considering user context with user contextual profile to suggest appropriate collaborators. In this article, we present the user contextual profile that we established and how it can be used to generate recommendations for collaborations in digital environments.
Self-Enhanced Convolutional Network for Facial Video Hallucination

As a domain-specific super-resolution problem, facial image hallucination has enjoyed a series of breakthroughs thanks to the advances of deep convolutional neural networks. However, the direct migration of existing methods to video is still difficult to achieve good performance due to its lack of alignment and consistency modelling in temporal domain. Taking advantage of high inter-frame dependency in videos, we propose a selfenhanced convolutional network for facial video hallucination. It is implemented by making full usage of preceding super-resolved frames and a temporal window of adjacent low-resolution frames. Specifically, the algorithm first obtains the initial high-resolution inference of each frame by taking into consideration a sequence of consecutive low-resolution inputs through temporal consistency modelling. It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames. Quantitative and qualitative evaluations demonstrate the superiority of the proposed algorithm against state-of-theart methods. Moreover, our algorithm also achieves excellent performance in the task of general video super-resolution in a single-shot setting.
Using Blockchain and IoT Technologies to Enhance Intellectual Property Protection

The Blockchain technology provides a way to record transactions or any digital interaction that is designed to be secure, tamper-proof, transparent, highly resistant to outages, traceable and auditable. The Internet of Things (IoT) technology is able to link computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and provides the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. These features encourage us to explore the combined application of IoT and Blockchain-based technology. In this paper, we propose a system architecture of blockchain and IoT based intellectual property protection system, which can process three types of intellectual property: 1) Patents, Copyrights, Trademarks etc.; 2) Industrial design, Trade dress, Craft works, Trade secrets etc.; and 3) Plant variety rights, Geographical indications etc. Using blockchain P2P network and IoT devices, the system can help us to establish a trusted, self-organized, open and ecological intellectual property protection system. To the best of our knowledge, this is the first work that applying blockchain technology and IoT technology on traditional intellectual property protection and trade ecosystem.
Two-qubit trace-norm geometric discord: the complete solution

We present the complete solution of the problem of determination of trace-norm geometric discord for arbitrary two-qubit state. Final answer is achieved due to effective reduction of the problem to the study of critical points of certain mapping depending on projectors. Our results are illustrated on various, also new, families of two-qubit states and compared to already known special solutions.