Reviewerly/exHarmony huggingface dataset:
Saturating the quantum Cramér–Rao bound using LOCC

The quantum Cramér-Rao bound (QCRB) provides an ultimate precision limit allowed by quantum mechanics in parameter estimation.Given any quantum state dependent on a single parameter, there is always a positive-operator valued measurement (POVM) saturating the QCRB.However, the QCRB-saturating POVM cannot always be implemented efficiently, especially in multipartite systems.In this paper, we show that the POVM based on local operations and classical communication (LOCC) is QCRB-saturating for arbitrary pure states or rank-two mixed states with varying probability distributions over fixed eigenbasis.Local measurements without classical communication, however, is not QCRB-saturating in general.Local measurements (LM) (Fig. 1b), performed separately on each subsystem, were shown to saturate the QCRB in many cases [1,[36][37][38].For example, it is proven in Ref.[1] that for GHZ-type states evolving under local
Consistency and identifiability of football teams: a network science perspective

We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams' matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match.
The importance and differences of analytical procedures' application for auditing blockchain technology between external and internal auditors in Croatia

Nowadays audit profession is faced with an excessive evolution of the information and communication technology (ICT). The effects of ICT on auditing are dual. On the one hand, auditing is faced with the digitalization of companies' business operations, and on the other, auditors must be able to adapt their methodologies to these changes in order to be able to audit implemented ICT in the companies' business operations. One of the latest ICT innovations includes the application of blockchain technology (BCT) in different business operations of a company, which represents the object of auditing. In order to audit BCT, auditors must apply appropriate audit procedures, whereas analytical procedures (APs) represent the most useful one. The subjects of this paper are external and internal auditors, and their application of APs for auditing implemented BCT in the companies' business operations, in Croatia. Therefore, the main objective of this paper is to investigate the differences in the APs' application and its usefulness for auditing BCT, as an emerging ICT, between external and internal auditors. To investigate the main objective of the paper, desk research and survey research were conducted. Overall results indicated that external and internal auditors in Croatia are aware that auditing BCT requires the application of advanced APs, for what they need to possess excellent knowledge about APs and BCT. Obtained results showed that auditors in Croatia possess below-average knowledge about APs and BCT. Therefore, the necessity for specialized education of external and internal auditors is inevitable. Regarding the investigation of differences in the readiness to audit BCT between external and internal auditors in Croatia, the results confirmed that external auditors are more ready to audit BCT than internal auditors. Finally, research results confirmed that the application of advanced APs in audit engagements will increase the efficiency and effectiveness of companies' business operations supported by the BCT.
Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels. The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches. To overcome these drawbacks, the authors propose a novel two-stage framework. In the first step, sub-bands of wavelets of a low-resolution image are obtained using the dual-tree complex wavelet transform. Criminisi algorithm and auto-regression technique are then applied to these sub-bands to inpaint the missing regions. The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast. In the second step, the image is enhanced using super-resolution technique. The process of down-sampling, inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time. The framework is tested on video sequences by comparing and analysing the structural similarity index matrix, peak-signal-to-noise ratio, visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms. The experimental analysis gives visually pleasing results for object removal and error concealment.
Does Infrastructure as Code Adhere to Semantic Versioning? An Analysis of Ansible Role Evolution

Ansible, a popular Infrastructure-as-Code platform, provides reusable collections of tasks called roles. Roles are often contributed by third parties, and like general-purpose libraries, they evolve. As such, new releases of roles need to be tagged with version numbers, for which Ansible recommends adhering to the semantic versioning format. However, roles significantly differ from general-purpose libraries, and it is not yet known what constitutes a breaking change or the addition of a feature to a role. Consequently, this can cause confusion for clients of a role and new role contributors. To alleviate this issue, we perform an empirical study on semantic versioning in Ansible roles to uncover the types of changes that trigger certain types of version bumps. We collect a dataset of over 70000 version increments spanning upwards of 7800 Ansible roles. Moreover, we design a novel structural model for these roles, and implement a domainspecific structural change extraction algorithm to calculate structural difference metrics. Afterwards, we quantitatively investigate the state of semantic versioning in Ansible roles and identify the most commonly changed components. Then, using the structural difference metrics, we train a Random Forest classifier to predict applicable version bumps for Ansible role releases. Lastly, we confirm our empirical findings with a developer survey. Our observations show that although most Ansible role developers follow the semantic versioning format, it appears that they do not always consistently follow the same rules when selecting the version bump to apply.
Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction

Imbalanced data are a major factor for degrading the performance of software defect models. Software defect dataset is imbalanced in nature, i.e., the number of non-defect-prone modules is far more than that of defect-prone ones, which results in the bias of classifiers on the majority class samples. In this paper, we propose a novel credibility-based imbalance boosting (CIB) method in order to address the class-imbalance problem in software defect proneness prediction. The method measures the credibility of synthetic samples based on their distribution by introducing a credit factor to every synthetic sample, and proposes a weight updating scheme to make the base classifiers focus on synthetic samples with high credibility and real samples. Experiments are performed on 11 NASA datasets and nine PROMISE datasets by comparing CIB with MAHAKIL, AdaC2, AdaBoost, SMOTE, RUS, No sampling method in terms of four performance measures, i.e., area under the curve (AUC), F1, AGF, and Matthews correlation coefficient (MCC). Wilcoxon sign-ranked test and Cliff’s δ are separately used to perform statistical test and calculate effect size. The experimental results show that CIB is a more promising alternative for addressing the class-imbalance problem in software defect-prone prediction as compared with previous methods.
A Permissioned Blockchain-Based Platform for Education Certificate Verification

Distance education has become an important learning method for students. Nevertheless, online teaching is difficult to ensure the traceability of student activities. The difficulty in managing the non-instantaneous diploma verification and digital/paper files have always been severe problems in education management. The education information stored within the current centralized system is simply leaked and cannot be guaranteed in terms of impartiality and authenticity. In the present paper, a digital education certificate prototype is designed utilizing the permissioned blockchain of PKI-CA, digest algorithm, and interactive data authentication by digital signatures. The digital certificates can be issued by the system realizing the instant certificate verification between students and third parties via QR codes or dynamic authorization codes. The test results indicated that 100% correct work of the prototype, with the significant throughputs of getting and creating transactions are respectively 1982.6 tps and 263.9 tps on-chain during the test cycle.
Public opinion mining using natural language processing technique for improvisation towards smart city

In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information's, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.
Students' Proof Assistant (SPA)

The Students' Proof Assistant (SPA) aims to both teach how to use a proof assistant like Isabelle and also to teach how reliable proof assistants are built. Technically it is a miniature proof assistant inside the Isabelle proof assistant. In addition we conjecture that a good way to teach structured proving is with a concrete prover where the connection between semantics, proof system, and prover is clear. The proofs in Lamport's TLAPS proof assistant have a very similar structure to those in the declarative prover SPA. To illustrate this we compare a proof of Pelletier's problem 43 in TLAPS, Isabelle/Isar and SPA. We also consider Pelletier's problem 34, also known as Andrews's Challenge, where students are encouraged to develop their own justification function and thus obtain a lot of insight into the proof assistant. Although SPA is fully functional we have so far only used it in a few educational scenarios.
Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World

Atlanta world holds for the scenes composed of a vertical dominant direction and several horizontal dominant directions. Vanishing point (VP) is the intersection of the image lines projected from parallel 3D lines. In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown. Existing approaches are prone to missing partial inliers, rely on prior knowledge of the number of VPs, and/or lead to low efficiency. To overcome these limitations, we propose the novel mine-and-stab (MnS) algorithm and embed it in the branch-and-bound (BnB) algorithm. Different from BnB that iteratively branches the full parameter intervals, our MnS directly mines the narrow sub-intervals and then stabs them by probes. We simultaneously search for the vertical VP by BnB and horizontal VPs by MnS. The proposed collaboration between BnB and MnS guarantees global optimality in terms of maximizing the number of inliers. It can also automatically determine the number of VPs. Moreover, its efficiency is suitable for practical applications. Experiments on synthetic and real-world datasets showed that our method outperforms state-of-the-art approaches in terms of accuracy and/or efficiency.
The Landscape of R Packages for Automated Exploratory Data Analysis

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of twelve popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.
Using POMDPs for learning cost sensitive decision trees

In classification, an algorithm learns to classify a given instance based on a set of observed attribute values. In many real world cases testing the value of an attribute incurs a cost. Furthermore, there can also be a cost associated with the misclassification of an instance. Cost sensitive classification attempts to minimize the expected cost of classification, by deciding after each observed attribute value, which attribute to measure next. In this paper we suggest Partially Observable Markov Decision Processes (POMDPs) as a modeling tool for cost sensitive classification. POMDPs are typically solved through a policy over belief states. We show how a relatively small set of potentially important belief states can be identified, and define an MDP over these belief states. To identify these potentially important belief states, we construct standard decision trees over all attribute subsets, and the leaves of these trees become the state space of our tree-based MDP. At each phase we decide on the next attribute to measure, balancing the cost of the measurement and the classification accuracy. We compare our approach to a set of previous approaches, showing our approach to work better for a range of misclassification costs.
Nonadaptive fault-tolerant verification of quantum supremacy with noise

Quantum samplers are believed capable of sampling efficiently from distributions that are classically hard to sample from. We consider a sampler inspired by the classical Ising model. It is nonadaptive and therefore experimentally amenable. Under a plausible conjecture, classical sampling upto additive errors from this model is known to be hard. We present a trap-based verification scheme for quantum supremacy that only requires the verifier to prepare single-qubit states. The verification is done on the same model as the original sampler, a square lattice, with only a constant overhead. We next revamp our verification scheme in two distinct ways using fault tolerance that preserves the nonadaptivity. The first has a lower overhead based on error correction with the same threshold as universal quantum computation. The second has a higher overhead but an improved threshold (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1.97</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math>) based on error detection. We show that classically sampling upto additive errors is likely hard in both these schemes. Our results are applicable to other sampling problems such as the Instantaneous Quantum Polynomial-time (IQP) computation model. They should also assist near-term attempts at experimentally demonstrating quantum supremacy and guide long-term ones.
Semantic-consistent cross-modal hashing for large-scale image retrieval

With an emphasis on saving storage and computation costs, hashing learning has got considerable success in cross-modal image retrieval. Most pioneer efforts in this direction either consider similarity across modalities or leverage discriminative information of the class labels to learn the common latent representation. However, the learnt representation only contains coherent semantics across modalities but could not be line with class-wise semantic structure. To attack this issue, we propose a semantic-consistent cross-modal hashing (SCCH) to take class semantic structure into consideration. It not only ensures the common representation to be consistent across modalities by directly learning the shared binary codes of samples via rotation transformation, but also restricts the class-wise representation line with the learnt binary codes. In this way, SCCH jointly preserves class semantic structure and avoids large quantization errors caused by the approximation of real values to binary codes. Moreover, we efficiently optimize SCCH via an iterative algorithm. Experiments on three publicly datasets demonstrate the superiority of SCCH against several representative start-of-the-art counterparts in light of performance metrics.
Attention-aware concentrated network for saliency prediction

This paper presents a biologically-inspired saliency prediction method to imitate two main characteristics of the human perception process: focalization and orienting. The proposed network, named ACNet is composed of two modules. The first one is an essential concentrated module (CM), which assists the network to “see” images with appropriate receptive fields by perceiving rich multi-scale multi-receptive-field contexts of high-level features. The second is a parallel attention module (PAM), which explicitly guides the network to learn “what” and “where” is salient by simultaneously capturing global and local information with channel-wise and spatial attention mechanisms. These two modules compose the core component of the proposed method, named ACBlock, which is cascaded to progressively refine the inference of saliency estimation in a manner similar to that humans zoom in their lens to focus on the saliency. Experimental results on seven public datasets demonstrate that the proposed ACNet outperforms the state-of-the-art models without any prior knowledge or post-processing.
Universal linear optical operations on discrete phase-coherent spatial modes with a fixed and non-cascaded setup

Linear optical operations are fundamental and significant for both quantum mechanics and classical technologies. We demonstrate a non-cascaded approach to perform arbitrary linear operations for N-dimensional phase-coherent spatial modes using meticulously designed phase gratings. As implemented on spatial light modulators (SLMs), the unitary transformation matrix has been realized with dimensionalities ranging from 7 to 24 and the corresponding fidelities are from 95.1% to 82.1%. For the non-unitary operators, a matrix is presented for the tomography of a 4-level quantum system with a fidelity of 94.9%. Thus, the linear operator has been successfully implemented with much higher dimensionality than that in previous reports. It should be mentioned that our approach is not limited to SLMs and can be easily applied on other devices. Thus, our proposal could provide another option to perform linear operations with a simple, fixed, error-tolerant and scalable scheme.
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data

Electronic health records (EHRs) contain structured and unstructured data of significant clinical and research value. Various machine learning approaches have been developed to employ information in EHRs for risk prediction. The majority of these attempts, however, focus on structured EHR fields and lose the vast amount of information in the unstructured texts. Deep neural networks, on the other hand, gained tremendous momentum in knowledge discovery from EHR texts, while there are very seldom studies that used of both free-texts and the structured information in EHRs for clinical prediction. To exploit the potential information captured in EHRs, in this study we propose MI-BiLSTM, a multimodal bidirectional long short-term memory-based framework for cardiovascular risk prediction that integrates medical texts and structured clinical information. The MI-BiLSTM framework concatenates word embeddings from x-ray reports to classical clinical predictors from the Second Manifestations of ARTerial disease (SMART) study [1], before applying them to a final fully connected neural network. In the experiments, by employing the proposed framework, we compared performances of different deep neural network architectures on data of 5603 patients using 5-fold cross validation. Evaluated on the SMART study, we demonstrate the clinical relevance of integrating text features and classical predictors for cardiovascular risk prediction for patients with manifest vascular disease or at high--risk for cardiovascular disease. Our results show that the MI-BiLSTM framework using text data in addition to laboratory values outperforms deep learning models using only known clinical predictors. In future, we will focus on expanding our multimodal framework to import knowledge from available medical ontologies to enhance the quality of clinical decision making in risk prediction models.
Differential Evolution with Linear Bias Reduction in Parameter Adaptation

In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve the exploration properties at the beginning of the search and speed up the exploitation at the end of the search. As a basic algorithm, the L-SHADE approach is considered, as well as its modifications, namely the jSO and DISH algorithms. The experiments are performed on the CEC 2017 and 2020 bound-constrained benchmark problems, and the performed statistical comparison of the results demonstrates that the linear bias reduction allows significant improvement of the differential evolution performance for various types of optimization problems.