Reviewerly/exHarmony huggingface dataset:
An image super-resolution reconstruction model based on fractional-order anisotropic diffusion equation

&lt;abstract&gt;&lt;p&gt;The image denoising model based on anisotropic diffusion equation often appears the staircase effect while image denoising, and the traditional super-resolution reconstruction algorithm can not effectively suppress the noise in the image in the case of blur and serious noise. To tackle this problem, a novel model is proposed in this paper. Based on the original diffusion equation, we propose a new method for calculating the adaptive fidelity term and its coefficients, which is based on the relationship between the image gradient and the diffusion function. It is realized that the diffusion speed can be slowed down by adaptively changing the coefficient of the fidelity term, and it is proved mathematically that the proposed fractional adaptive fidelity term will not change the existence and uniqueness of the solution of the original model. At the same time, washout filter is introduced as the control item of the model, and a new model of image super-resolution reconstruction and image denoising is constructed. In the proposed model, the order of fractional differential will be determined adaptively by the local variance of the image. And we give the numerical calculation method of the new model in the frequency domain by the method of Fourier transform. The experimental results show that the proposed algorithm can better prevent the staircase effect and achieve better visual effect. And by introducing washout filter to act as the control of the model, the stability of the system can be improved and the system can converge to a stable state quickly.&lt;/p&gt;&lt;/abstract&gt;
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior

Abstract We study continual learning for natural language instruction generation, by observing human users’ instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural language. We compare user execution of generated instructions to the original system intent as an indication to the system’s success communicating its intent. We show how to use this signal to improve the system’s ability to generate instructions via contextual bandit learning. In interaction with real users, our system demonstrates dramatic improvements in its ability to generate language over time.
The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers

In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams. ContrXT is available as a pip package and API.
A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power

ABSTRACT Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights – while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event that includes a damped oscillation and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelization, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.
Block-coherence measures and coherence measures based on positive-operator-valued measures

Abstract We study block-coherence measures based on the resource theory of block-coherence and coherence measures based on positive-operator-valued measures (POVM). Several block-coherence measures are presented, including the block-coherence measure based on maximum relative entropy, the one-shot block-coherence cost under maximally block-incoherent operations, and the coherence measure based on coherent rank. Their relationships are obtained. Moreover, we describe the deterministic coherence dilution process by constructing block-incoherent operations. Based on the POVM coherence resource theory, we also propose two coherence measures and analyze their relationship.
Exemplification Modeling: Can You Give Me an Example, Please?

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.
Toward a Complete Software Stack to Integrate Quantum Key Distribution in a Cloud Environment

The coming advent of Quantum Computing promises to jeopardize current communications security, undermining the effectiveness of traditional public-key based cryptography. Different strategies (Post-Quantum or Quantum Cryptography) have been proposed to address this problem. Many techniques and algorithms based on quantum phenomena have been presented in recent years; the most relevant example is the introduction of Quantum Key Distribution (QKD). This approach allows to exchange cryptographic keys among parties and does not suffer from the development of quantum computation. Problems arise when this technique has to be deployed and combined with modern distributed infrastructures that heavily depend on cloud and virtualisation paradigms. This paper addresses the issue by presenting a new software stack that effortlessly introduces QKD in such environments. This software stack allows for agnostic integration, monitoring, and management of QKD, independent from a specific vendor or technology. Furthermore, a QKD simulator is presented, designed, and tested. This latter contribution is suitable as a low-level testing device, as an independent software module to check QKD protocols, and as a testbed to identify future practical enhancements.
Deep Unified Cross-Modality Hashing by Pairwise Data Alignment

With the increasing amount of multimedia data, cross-modality hashing has made great progress as it achieves sub-linear search time and low memory space. However, due to the huge discrepancy between different modalities, most existing cross-modality hashing methods cannot learn unified hash codes and functions for modalities at the same time. The gap between separated hash codes and functions further leads to bad search performance. In this paper, to address the issues above, we propose a novel end-to-end Deep Unified Cross-Modality Hashing method named DUCMH, which is able to jointly learn unified hash codes and unified hash functions by alternate learning and data alignment. Specifically, to reduce the discrepancy between image and text modalities, DUCMH utilizes data alignment to learn an auxiliary image to text mapping under the supervision of image-text pairs. For text data, hash codes can be obtained by unified hash functions, while for image data, DUCMH first maps images to texts by the auxiliary mapping, and then uses the mapped texts to obtain hash codes. DUCMH utilizes alternate learning to update unified hash codes and functions. Extensive experiments on three representative image-text datasets demonstrate the superiority of our DUCMH over several state-of-the-art cross-modality hashing methods.
Two-Stage Fine-Grained Text-Level Sentiment Analysis Based on Syntactic Rule Matching and Deep Semantic

Aiming at the problem that traditional text-level sentiment analysis methods usually ignore the emotional tendency corresponding to the object or attribute. In this paper, a novel two-stage fine-grained text-level sentiment analysis model based on syntactic rule matching and deep semantics is proposed. Based on analyzing the characteristics and difficulties of fine-grained sentiment analysis, a two-stage fine-grained sentiment analysis algorithm framework is constructed. In the first stage, the objects and its corresponding opinions are extracted based on syntactic rules matching to obtain preliminary objects and opinions. The second stage based on deep semantic network to extract more accurate objects and opinions. Aiming at the problem that the extraction result contains multiple objects and opinions to be matched, an object-opinion matching algorithm based on the minimum lexical separation distance is proposed to achieve accurate pairwise matching. Finally, the proposed algorithm is evaluated on several public datasets to demonstrate its practicality and effectiveness.
Assessing Compressive Strength of Concrete with Extreme Learning Machine

Manual estimation of compressive strength of concrete (CSC) is time consuming and expensive. Soft computing techniques are found better to statistical methods applied to this problem. However, sophisticated prediction models are still lacking and need to be explored. Extreme learning machine (ELM) is a faster and better learning method for artificial neural networks (ANNs) with solitary hidden layer and has enhanced generalization capacity. This article presents an ELM-based forecast for efficient prediction of CSC. A publicly available dataset from UCI repository is used to develop and access the performance of the model. The prediction accuracy of ELM is compared with few machine learning methods such as back propagation neural network (BPNN), support vector machine (SVM), auto-regressive integrated moving average (ARIMA), and least squared estimation (LSE). A comparative study for the prediction of CSC at the curing ages of 28, 56, and 91 days has been carried out using all models. The experimental findings from ELM-based forecasting demonstrate its ability in predicting CSC in a robust manner. On an average, it achieves lowest MAPE of 0.048024, ARV of 0.052872, U of Theil’s statistics (UT) of 0.038772, NMSE of 0.058522, and standard deviation (SD) of 0.256267. Comparative analysis of simulation results and statistical significance test suggests the superiority of ELM-based CSC prediction.
Quantitative and semi–quantitative analyses using a portable energy dispersive X–ray fluorescence spectrometer: Geochemical applications in fault rocks, lake sediments, and event deposits

Quantitative and semi–quantitative procedures using a portable energy dispersive X–ray fluorescence spectrometer (portable XRF) were applied to geochemical studies in fault rocks, lake sediments, and soils, including paleotsunami deposits. The results obtained are as follows:
Optimal local unitary encoding circuits for the surface code

The surface code is a leading candidate quantum error correcting code, owing to its high threshold, and compatibility with existing experimental architectures. Bravyi et al. (2006) showed that encoding a state in the surface code using local unitary operations requires time at least linear in the lattice size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>L</mml:mi></mml:math>, however the most efficient known method for encoding an unknown state, introduced by Dennis et al. (2002), has <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> time complexity. Here, we present an optimal local unitary encoding circuit for the planar surface code that uses exactly <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mi>L</mml:mi></mml:math> time steps to encode an unknown state in a distance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>L</mml:mi></mml:math> planar code. We further show how an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> complexity local unitary encoder for the toric code can be found by enforcing locality in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>-depth non-local renormalisation encoder. We relate these techniques by providing an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> local unitary circuit to convert between a toric code and a planar code, and also provide optimal encoders for the rectangular, rotated and 3D surface codes. Furthermore, we show how our encoding circuit for the planar code can be used to prepare fermionic states in the compact mapping, a recently introduced fermion to qubit mapping that has a stabiliser structure similar to that of the surface code and is particularly efficient for simulating the Fermi-Hubbard model.
Papaya: Global Typestate Analysis of Aliased Objects

Typestates are state machines used in object-oriented programming to specify and verify correct order of method calls on an object. To avoid inconsistent object states, typestates enforce linear typing, which eliminates—or at best limits—aliasing. However, aliasing is an important feature in programming, and the state-of-the-art on typestates is too restrictive if we want typestates to be adopted in real-world software systems.
Energy characteristics of the uav-based georadar for searching a water layer in aroid areas.

In this paper we consider the calculation of the potential of a georadar on board an unmanned aerial vehicle designed to search for groundwater in arid and desert regions of Mongolia. The soil types prevailing in the regions under study were taken as soils as a medium for the propagation of radio waves. A feature of soils in arid and deserted regions is that they have almost the same dielectric constant, with only a slight difference in specific attenuation due to low water content. Soils with possible typical models of changes in the dielectric constant of the soil along the depth are considered.
Experimental Bayesian estimation of quantum state preparation, measurement, and gate errors in multiqubit devices

We introduce a Bayesian method for the estimation of single qubit errors in quantum devices, and use it to characterize these errors on three 27-qubit superconducting qubit devices. We self-consistently estimate up to seven parameters of each qubit's state preparation, readout, and gate errors, analyze the stability of these errors as a function of time, and demonstrate easily implemented approaches for mitigating different errors before a quantum computation experiment. On the investigated devices we find non-negligible qubit reset errors that cannot be parametrized as a diagonal mixed state, but manifest as a coherent phase of a superposition with a small contribution from the qubit's excited state. We are able to mitigate such errors by applying pre-rotations on the initialized qubits, which we demonstrate with multi-qubit entangled states. Our results demonstrate that Bayesian estimation can resolve small parameters - including those pertaining to quantum gate errors - with a high relative accuracy, at a lower measurement cost as compared with standard characterization approaches.
Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.
DECIFE: Detecting Collusive Users Involved in Blackmarket Following Services on Twitter

The popularity of Twitter has fostered the emergence of various fraudulent user activities - one such activity is to artificially bolster the social reputation of Twitter profiles by gaining a large number of followers within a short time span. Many users want to gain followers to increase the visibility and reach of their profiles to wide audiences. This has provoked several blackmarket services to garner huge attention by providing artificial followers via the network of agreeable and compromised accounts in a collusive manner. Their activity is difficult to detect as the blackmarket services shape their behavior in such a way that users who are part of these services disguise themselves as genuine users. In this paper, we propose DECIFE, a framework to detect collusive users involved in producing 'following' activities through blackmarket services with the intention to gain collusive followers in return. We first construct a heterogeneous user-tweet-topic network to leverage the follower/followee relationships and linguistic properties of a user. The heterogeneous network is then decomposed to form four different subgraphs that capture the semantic relations between the users. An attention-based subgraph aggregation network is proposed to learn and combine the node representations from each subgraph. The combined representation is finally passed on to a hypersphere learning objective to detect collusive users. Comprehensive experiments on our curated dataset are conducted to validate the effectiveness of DECIFE by comparing it with other state-of-the-art approaches. To our knowledge, this is the first attempt to detect collusive users involved in blackmarket 'following services' on Twitter.
New Strategy for Higher Education for 2021-2030 in Bulgaria and Challenges for Its Implementation

A strategy for higher education has to begin and end with quality of higher education process itself, because this is the first and main criterion by which we may or may not be included in the world university rankings. The strategy started with quantitative parameters and so will end in this way - with quantitative results, numerous changes in law, etc. Defining the main national priorities is perhaps the most essential action in creating a document of this nature and there is no way to organize the priorities of any system, even if it is the most important for our society. The purpose of this publication is to examine the challenges the new Strategy for Higher Education for 2021-2030 in Bulgaria is facing.
Blockchain as a Countermeasure Solution for Security Threats of Healthcare Applications

Healthcare industry is digitising its healthcare operations and generating huge amounts of sensitive medical data to make prompt and informed decisions in patients' health diagnosis and care. The healthcare industry is subjected to a variety of security threats (e.g., data tampering, theft, and counterfeit drugs). Blockchain is gaining traction to address such security threats and improve data integrity by turning healthcare operations into decentralised, transparent, and immutable manners. However, there is conceptual ambiguity and semantic gaps about blockchain as a countermeasure solution for healthcare security threats. In this work, we use the web ontology language to create blockchain-based security ontology (HealthOnt) to remove conceptual ambiguity and semantic gaps. The HealthOnt offers coherent and formal information models that present blockchain as a countermeasure solution for security threats of traditional healthcare applications.