Reviewerly/exHarmony huggingface dataset:
Telepathology: An update on applications, latest advances, and current status in Indian scenario

Pathologists have been using their tool of trade, "the microscope," since the early 17th century, but now diagnostic pathology or tissue-based diagnosis is characterized by its high specificity and sensitivity. Technological telecommunication advances have revolutionized the face of medicine, and in pursuit of better health-care delivery, telepathology has emerged. Telepathology is the practice of diagnostic pathology performed at a distance, with images viewed on a video monitor rather than directly through the (light) microscope. This article aims to provide an overview of the field, including specific applications, practice, benefits, limitations, regulatory issues, latest advances, and a perspective on the current status of telepathology in Indian scenario based on literature review.
Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution

This paper aims to propose a candidate solution to the challenging task of single-image blind super-resolution (SR), via extensively exploring the potentials of learning-based SR schemes in the literature. The task is formulated into an energy functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The functional includes a so-called convolutional consistency term which incorporates a nonblind learning-based SR result to better guide the kernel estimation process, and a bi-[Formula: see text]-[Formula: see text]-norm regularization imposed on both the super-resolved sharp image and the nonparametric blur-kernel. A numerical algorithm is deduced via coupling the splitting augmented Lagrangian (SAL) and the conjugate gradient (CG) method. With the estimated blur-kernel, the final SR image is reconstructed using a simple TV-based nonblind SR method. The proposed blind SR approach is demonstrated to achieve better performance than [T. Michaeli and M. Irani, Nonparametric Blind Super-resolution, in Proc. IEEE Conf. Comput. Vision (IEEE Press, Washington, 2013), pp. 945–952.] in terms of both blur-kernel estimation accuracy and image ehancement quality. In the meanwhile, the experimental results demonstrate surprisingly that the local linear regression-based SR method, anchored neighbor regression (ANR) serves the proposed functional more appropriately than those harnessing the deep convolutional neural networks.
Universal quantum computation in the surface code using non-Abelian islands

The surface code is currently the primary proposed method for performing quantum error correction. However, despite its many advantages, it has no native method to fault-tolerantly apply non-Clifford gates. Additional techniques are therefore required to achieve universal quantum computation. Here we propose a hybrid scheme which uses small islands of a qudit variant of the surface code to enhance the computational power of the standard surface code. This allows the non-trivial action of the non-Abelian anyons in the former to process information stored in the latter. Specifically, we show that a nonstabilizer state can be prepared, which allows universality to be achieved.
Characterization of Equivariant Maps and Application to Entanglement Detection

We study equivariant linear maps between finite-dimensional matrix algebras, as introduced in Collins et al. (Linear Algebra Appl 555:398–411, 2018). These maps satisfy an algebraic property which makes it easy to study their positivity or k-positivity. They are therefore particularly suitable for applications to entanglement detection in quantum information theory. We characterize their Choi matrices. In particular, we focus on a subfamily that we call (a, b)-unitarily equivariant. They can be seen as both a generalization of maps invariant under unitary conjugation as studied by Bhat (Banach J Math Anal 5(2):1–5, 2011) and as a generalization of the equivariant maps studied in Collins et al. (2018). Using representation theory, we fully compute them and study their graphical representation and show that they are basically enough to study all equivariant maps. We finally apply them to the problem of entanglement detection and prove that they form a sufficient (infinite) family of positive maps to detect all k-entangled density matrices.
Comparative Document Summarisation via Classification

Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.
Elman-Based Forecaster Integrated by Adaboost Algorithm in 15 min and 24 h ahead Power Output Prediction Using PM 2.5 Values, PV Module Temperature, Hours of Sunshine, and Meteorological Data

Nowadays, with the depletion of fossil energy and deterioration of environmental quality, solar energy is perceived to be a renewable and clean energy.While developing rapidly all over the world, solar energy is also faced with many challenges resulting from its inherent properties.In order to reduce the impact on the grid and facilitate scheduling, it is a growing problem to build a feasible model to forecast PV power with high precision.Therefore, this paper proposes an Elman-based forecaster integrated by Adaboost algorithm, namely Adaboost + Elman.Before forecasting, input variables containing PM 2.5 values, temperature of the PV module, sunshine hours, and meteorological data are made using correlation, clustering, and discriminate analysis to avoid information redundancy and improve the generalization ability of the model.To verify the developed model's application to shortterm PV forecasting in two different time scales, data of Huangsi in 2016 are used for model construction and verification.An additional 7 models are introduced to make comparison.Experimental results prove that the proposed model is effective and practicable for two different scales of short-term PV power prediction.
Fringe visibility and distinguishability in two-path interferometer with an asymmetric beam splitter

We study the fringe visibility and the distinguishability of a general Mach-Zehnder interferometer with an asymmetric beam splitter. Both the fringe visibility V and the distinguishability D are affected by the input state of the particle characterized by the Bloch vector S=(Sx,Sy,Sz) and the second asymmetric beam splitter characterized by paramter /beta. For the total system is initially in a pure state, it is found that the fringe visibility reaches the upper bound and the distinguishability reaches the lower bound when cos(/beta) = -Sx. The fringe visibility obtain the maximum only if Sx = 0 and /beta = /pi/2 when the input particle is initially in a mixed state. The complementary relationship V2 + D2 <= 1 is proved in a general Mach-Zehnder interferometer with an asymmetric beam splitter, and the conditions for the equality are also presented.
Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content

Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process. In particular, our method exploits unsupervised semantic information in the learning process to capture the intrinsic similarity through a graph regularization. Moreover, we use Hilbert Schmidt Independence Criterion (HSIC) to explore the unsupervised semantic diversity information among multi-view contents of one web item. The overall objective is to minimize the loss function of multi-view non-negative matrix factorization that combines with an intra-semantic similarity graph regularizer and an inter-semantic diversity term. Compared with some state-of-the-art methods, we demonstrate the effectiveness of our proposed method on a large real-world dataset Doucom and the other three smaller datasets.
Considering Race a Problem of Transfer Learning

The following topics are dealt with: video signal processing; object detection; feature extraction; image classification; biometrics (access control); learning (artificial intelligence); object tracking; face recognition; convolutional neural nets; image motion analysis.
Complexity of Abstract Argumentation under a Claim-Centric View

Abstract argumentation frameworks have been introduced by Dung as part of an argumentation process, where arguments and conflicts are derived from a given knowledge base. It is solely this relation between arguments that is then used in order to identify acceptable sets of arguments. A final step concerns the acceptance status of particular statements by reviewing the actual contents of the acceptable arguments. Complexity analysis of abstract argumentation so far has neglected this final step and is concerned with argument names instead of their contents, i.e. their claims. As we outline in this paper, this is not only a slight deviation but can lead to different complexity results. We, therefore, give a comprehensive complexity analysis of abstract argumentation under a claim-centric view and analyse the four main decision problems under seven popular semantics. In addition, we also address the complexity of common sub-classes and introduce novel parameterisations – which exploit the nature of claims explicitly – along with fixed-parameter tractability results.
A Secure Model for Machine to Machine Device Domain Based Group in a Smart City Architecture

The new Internet of Things (IoT) applications are enabling Smart City initiatives worldwide.It provides the ability to remotely monitor, manage and control devices.The main features of a smart city include privacy of users and security of communication.The distributed architecture of IoT aims at providing location awareness and low-latency interactions to Machine-to-Machine (M2M) applications.In this context, the Long Term Evolution (LTE) technology and its evolutions are expected to play a major role as a communication infrastructure that guarantees low deployment costs and embedded security, we show how a network deployment that exploits Deviceto-Device (D2D) communications and Wireless Sensor Network (WSN), currently under definition within 3rd Generation Partnership Project (3GPP) and non-3GPP access network, can be employed to support efficient communication between M2M devices (nodes and smart objects) and the core network in LTE system.In this paper, we propose a secure group model include different authentication mechanisms combined between D2D and WSN technologies that allow the protection of M2M devices through strong encryption and authentication means, so that devices can benefit from high security functionalities without however having to execute computationally intensive operations with a low communication complexity.The results obtained show that our model firstly solve the initial key establishment and integrity problems in the presence of the inside adversaries in networks and offers best performance than the standard Bluetooth in D2D link.Secondly our proposed examine the area of authentication for sensor networks by make a combination between algorithms such as TinyPK and Timed Efficient Stream Loss-Tolerant Authentication (TESLA) in order to secure the link between sensors and achieve a high levels of security.
A Study of Digital Nativeness and Digital Productivity: Data from EFL and ESL Contexts

It is often argued that current generations use digital technologies in different ways than older ones do. In response to the need for research on how younger generations use new technologies, this paper surveys identifiable patterns of technology use in EFL (English as a foreign language) and ESL (English as a second language) learners in daily and academic contexts, with a focus on digital productivity. To collect data, 97 EFL learners and 30 ESL learners were given a survey developed by the researcher. The survey was validated using verbal probing as a cognitive validation strategy. The data were analysed using SPSS 18.0 through descriptive statistics, Welch–Satterthwaite test and correlation coefficients. The results revealed that access to technology is not a significant issue in both contexts. However, although ESL students are better at using technologies in daily life, they are no different from EFL students with respect to producing content for learning. The data also indicated that both EFL and ESL learners produced content using only well-established digital tools, and there is some individual variation within both cohort of students. The findings imply that it could be useful to introduce new technologies into educational context both at individual and instructional levels
Quantum walker as a probe for its coin parameter

In discrete-time quantum walk (DTQW) the walker's coin space entangles with the position space after the very first step of the evolution. This phenomenon may be exploited to obtain the value of the coin parameter $\ensuremath{\theta}$ by performing measurements on the sole position space of the walker. In this paper, we evaluate the ultimate quantum limits to precision for this class of estimation protocols and use this result to assess measurement schemes having limited access to the position space of the walker in one dimension. We find that the quantum Fisher information (QFI) of the walker's position space ${H}_{w}(\ensuremath{\theta})$ increases with $\ensuremath{\theta}$ and with time, which, in turn, may be seen as a metrological resource. We also find a difference in the QFI of bounded and unbounded DTQWs and provide an interpretation of the different behaviors in terms of interference in the position space. Finally, we compare ${H}_{w}(\ensuremath{\theta})$ to the full QFI ${H}_{f}(\ensuremath{\theta})$, i.e., the QFI of the walkers position plus coin state, and find that their ratio is dependent on $\ensuremath{\theta}$, but saturates to a constant value, meaning that the walker may probe its coin parameter quite faithfully.
Enhancing the Performance of the Advanced Encryption Standard (AES) Algorithm Using Multiple Substitution Boxes

This paper proposes for a modified version of the AES algorithm using multiple substitution boxes (S-Boxes). While many studies have been conducted specifically on modifying the S-Box, these studies were made to replace the Rijndael S-boxes in the AES cipher. We propose to implement two substitution boxes, where the first S-Box is the Rijndael S-box and will be used as is. The second S-Box was constructed through an XOR operation and affine transformation and will replace the MixColumns operation within the internal rounds in the cipher. Based on simulation testing conducted, it was found out that there is a significant difference in the speed performance between the two versions favouring the proposed AES algorithm using multiple S-Box. The findings also revealed that in both encryption and decryption processes, the AES-2SBox performed more efficiently at 27.638% and 108.369% respectively as compared to the original AES algorithm. However, when tested using the avalanche effect, the changes in the output bits were below the minimum expected rate.
Detecting non-Markovianity via uncertainty relations

We present a formalism for detection of non-Markovianity through uncertainty relations. We show that when there is an information back-flow to the system from its environment through CP-divisibility breaking, the Choi-states corresponding to the reduced system evolution contain at least one negative eigenvalue. The consequent break down of uncertainty relations for such states can be used to witness non-Markovian dynamics. We present some relevant examples of the phenomenon for qubit channels. We further prove that square of the variance of a suitable hermitian operator can act as a non-linear witness of non-Markovianity. We finally show that non-Markovianity is necessary in order to decrease the uncertainty of the states undergoing unital dynamics for qubits. This provides another method of certifying non-Markovianity.
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers

Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extraction by encoding the paragraph only once (one-pass). We build our solution on the pre-trained self-attentive (Transformer) models, where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with an entity-aware attention technique. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.
Cognitive challenges at the crime scene: The importance of social science research when introducing mobile technologies at the crime scene

• Crime scene investigations are accompanied by cognitive challenges. • Introducing technologies at crime scenes requires research into the human factor. • Mobile technologies can impede the investigation without studying the impact.
Towards Aggregating Weighted Feature Attributions

Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution. The former leverages training instances to describe how much influence a training point exerts on a test point, while the latter attempts to attribute value to the features most pertinent to a given prediction. In this work, we discuss an algorithm, AVA: Aggregate Valuation of Antecedents, that fuses these two explanation classes to form a new approach to feature attribution that not only retrieves local explanations but also captures global patterns learned by a model. Our experimentation convincingly favors weighting and aggregating feature attributions via AVA.
Dynamic Multi Agent System for Revising E-Learning Content Materia

The growth of the information and communication technologies has led to the appearance of new concepts, approach and disciplines. For learners, an e-learning system constitutes rich window to the knowledge. It presents a varied training, including different content material format (video, text, interactive content...) and diverse methods. In order to keep learners’ attention, e-learning system must provide good content's quality, including revised material and updated methods. In this perspective, we have implemented multi-agent system composed of three sort of agents ensuring a permanent revision to the e-learning content. The first one is called Checker Agent (CA). It checks the educational resources, and detects the outdated ones so as to be treated. The second agent is named Search Agent (SA). The task of this one is to look for recent contents and new teaching methods. Whereas, the third agent is called Updater Agent (UA). Its function consists on inserting the retrieved updates corresponding to each content. The communication between these agents is ensured by an XML files. In this paper, we have proposed an implementation of the first part of our system. Namely, the checking process of e-learning curriculum by implementing the CA algorithm. And the integration process by implementing the UA algorithm. As result, the tests and experimentations done in this context have proved the effectiveness of the proposed solution, and revealed positive results both in term of learning process and learners’ feedback.
Field Dynamics Inference for Local and Causal Interactions

Abstract Inference of fields defined in space and time from observational data is a core discipline in many scientific areas. This work approaches the problem in a Bayesian framework. The proposed method is based on statistically homogeneous random fields defined in space and time and demonstrates how to reconstruct the field together with its prior correlation structure from data. The prior model of the correlation structure is described in a non‐parametric fashion and solely builds on fundamental physical assumptions such as space‐time homogeneity, locality, and causality. These assumptions are sufficient to successfully infer the field and its prior correlation structure from noisy and incomplete data of a single realization of the process as demonstrated via multiple numerical examples.
Learning Sparse Graphs for Prediction of Multivariate Data Processes

We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross validation or parameter tuning by building upon a hyperparameter-free framework. Our approach does not require the graph to be undirected and also accommodates varying noise levels across different nodes. Experiments using real-world datasets show that the proposed method offers significant performance gains in prediction, in comparison with the graphs frequently associated with these datasets.
User Behavior Modeling for Web Image Search

Web-based image search engines differ from Web search engines greatly. The intents or goals behind human interactions with image search engines are different. In image search, users mainly search images instead of Web pages or online services. It is essential to know why people search for images because user satisfaction may vary as intent varies. Furthermore, image search engines show results differently. For example, grid-based placement is used in image search instead of the linear result list, so that users can browse result list both vertically and horizontally. Different user intents and system UIs lead to different user behavior. Thus, it is hard to apply standard user behavior models developed for general Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the lab-based user study, field study and commercial search log analysis. We then propose user behavior models based on the observation from data analysis to improve the performance of Web image search engines.