Reviewerly/exHarmony huggingface dataset:
Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomenon of “fake news

Introduction. The English-language databases PubMed/MEDLINE and Embase are valuable information resources for finding original publications in basic and clinical medicine. Currently, there are no artificial intelligence systems to evaluate the quality of these publications. Aim. Development and testing of a system for sentiment analysis (i.e. analysis of emotional modality) of biomedical publications. Materials and methods. The technique of analysis of the "Big data" of biomedical publications was formulated on the basis of the topological theory of sentiment analysis. Algorithms have been developed that allow for the classification of texts from 16 sentiment classes with 90% accuracy (manipulative speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressive text, negative emotional background, etc.). Based on the algorithms, a scale for assessing the sentiment quality of research ( β -score) is proposed. Results. Abstracts of 19.9 million publications registered in PubMed/MEDLINE over the past 50 years (1970–2019) were analyzed. It was shown that publications with low sentiment quality (the value of the β -score of the text is less than zero, which corresponds to the prevalence of manipulative and negative sentiments in the text) comprise only 18.5% (3.68 out of 19.9 million). The greatest values of the β -score were characterized by publications on sports medicine, systems biology, nutrition, on the use of applied mathematics and data mining in medicine. The rubrication of the entire array of publications by 27,840 headings (MESH-system of PubMed/MEDLINE) indicated an increase in the β -score by years (i.e., the positive dynamics of sentiment quality of the texts of publications) for 27,090 of the studied headings. The most intense positive dynamics was found for research in genetics, physiology, pharmacology, and gerontology. 249 headings with sharply negative dynamics of sentiment quality and with a pronounced increase in the manipulative sentiments characteristic of the tabloid press were highlighted. Separate assessments of international experts are presented that confirm the patterns identified. Conclusion. The proposed artificial intelligence system allows a researcher to make an effective assessment of the sentiment quality of biomedical research papers, filtering out potentially inappropriate publications disguised as "evidence-based".
A Combined Measurement Method for Large-Size Aerospace Components

Automated and high-accuracy three-dimensional (3D) shape measurement is required in quality control of large-size components for the aerospace industry. To eliminate the contradiction between global measurement and local precision measurement control in 3D digitalization for the key local features of the large-size components, a combined measurement method is proposed, including a 3D scanner, a laser tracker, and an industrial robot used as an orienting device, to achieve high-accuracy measurement. As for improving the overall measurement accuracy, an accurate calibration method based on coordinate optimization of common points (COCP) and coordinate optimization of global control points (COGP) is proposed to determine the coordinate systems. Firstly, a coordinate optimization method of common points (COCP) is recommended. Then, a coordinate optimization method of global control points (COGP) based on the angular constraint is proposed for minimizing the measurement errors and improving the measurement accuracy of the position and orientation of the 3D scanner. Finally, a combined measurement system is established, and validation experiments are carried out in laboratory within a distance of 4 m. The calibration experiment results demonstrate that the max and mean errors of the coordinate transformation have been reduced from 0.037 and 0.022 mm to 0.021 and 0.0122 mm. Additionally, the measurement experiment results also show that the combined measurement system features high accuracy.
Analyzing photon-count heralded entanglement generation between solid-state spin qubits by decomposing the master-equation dynamics

We analyze and compare three different schemes that can be used to generate entanglement between spin qubits in optically-active single solid-state quantum systems. Each scheme is based on first generating entanglement between the spin degree of freedom and either the photon number, the time bin, or the polarization degree of freedom of photons emitted by the systems. We compute the time evolution of the entanglement generation process by decomposing the dynamics of a Markovian master equation into a set of propagation superoperators conditioned on the cumulative detector photon count. We then use the conditional density operator solutions to compute the efficiency and fidelity of the final spin-spin entangled state while accounting for spin decoherence, optical pure dephasing, spectral diffusion, photon loss, phase errors, detector dark counts, and detector photon number resolution limitations. We find that the limit to fidelity for each scheme is restricted by the mean wavepacket overlap of photons from each source, but that these bounds are different for each scheme. We also compare the performance of each scheme as a function of the distance between spin qubits.
Conversational BI

Business intelligence (BI) applications play an important role in the enterprise to make critical business decisions. Conversational interfaces enable non-technical enterprise users to explore their data, democratizing access to data significantly. In this paper, we describe an ontology-based framework for creating a conversation system for BI applications termed as Conversational BI. We create an ontology from a business model underlying the BI application, and use this ontology to automatically generate various artifacts of the conversation system. These include the intents, entities, as well as the training samples for each intent. Our approach builds upon our earlier work, and exploits common BI access patterns to generate intents, their training examples and adapt the dialog structure to support typical BI operations. We have implemented our techniques in Health Insights (HI) , an IBM Watson Healthcare offering, providing analysis over insurance data on claims. Our user study demonstrates that our system is quite intuitive for gaining business insights from data. We also show that our approach not only captures the analysis available in the fixed application dashboards, but also enables new queries and explorations.
A Novel Dual Image Watermarking Technique Using Homomorphic Transform and DWT

Abstract In this paper a new technique of dual image watermarking is proposed for protection of ownership rights which utilizes salient properties of homomorphic transform (HT), discrete wavelet transform (DWT), singular value decomposition (SVD) and Arnold transform (AT). In embedding algorithm host image is splitted into reflectance and illumination components using HT, DWT is further applied to the reflectance component resulting in frequency subbands (HL and LH) which are transformed by SVD. Two image watermarks are selected for embedding process whereas security of proposed algorithm is strengthen by performing scrambling of second watermark through AT. Both watermarks are transformed with DWT and SVD. Singular values (SVs) of both transformed watermark are embedded into SVs of host image. Simulation results clearly signifies for high robustness and imperceptibility of proposed algorithm as it is examined under various attacks. Superiority of proposed technique is illustrated by comparing it with other reported methods.
Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

Abstract An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learner’s actual performance on the presented items, causing a “cold start” during which the system is poorly adjusted to the situation. In this study, we implemented several strategies for mitigating this cold start problem in an adaptive fact learning system and experimentally tested their effect on learning performance. The strategies included predicting difficulty for individual learner-fact pairs, individual learners, individual facts, and the set of facts as a whole. We found that cold start mitigation improved learning outcomes, provided that there was sufficient variability in the difficulty of the study material. Informed individualised predictions allowed the system to schedule learners’ study time more effectively, leading to an increase in response accuracy during the learning session as well as improved retention of the studied items afterwards. Our findings show that addressing the cold start problem in adaptive learning systems can have a real impact on learning outcomes. We expect this to be particularly valuable in real-world educational settings with large individual differences between learners and highly diverse materials.
Coronary artery segmentation in angiographic videos utilizing spatial-temporal information

Abstract Background Coronary artery angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiographic images or videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. Methods This article proposes a novel coronary artery segmentation framework that combines a three–dimensional (3D) convolutional input layer and a two–dimensional (2D) convolutional network. Instead of a single input image in the previous medical image segmentation applications, our framework accepts a sequence of coronary angiographic images as input, and outputs the clearest mask of segmentation result. The 3D input layer leverages the temporal information in the image sequence, and fuses the multiple images into more comprehensive 2D feature maps. The 2D convolutional network implements down–sampling encoders, up–sampling decoders, bottle–neck modules, and skip connections to accomplish the segmentation task. Results The spatial–temporal model of this article obtains good segmentation results despite the poor quality of coronary angiographic video sequences, and outperforms the state–of–the–art techniques. Conclusions The results justify that making full use of the spatial and temporal information in the image sequences will promote the analysis and understanding of the images in videos.
Robust measurement fusion steady-state estimator design for multisensor networked systems with random two-step transmission delays and missing measurements

In this paper, we study the centralized fusion (CF) and weighted measurement fusion (WMF) robust steady-state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, two-step random delays, missing measurements, and uncertain noise variances. By using a model transformation approach consisting of augmented approach, de-randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi-model multisensor system with only uncertain noise variances. By introducing an augmented state vector, and applying the weighted least squares (WLS) algorithm, the CF and WMF systems are obtained. According to the minimax robust estimation principle, based on the worst-case fusion systems with conservative upper bounds of uncertain noise variances, the CF and WMF robust steady-state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Their robustness is proved by using a combination method consisting of augmented noise approach, matrix representation approach of quadratic form, and Lyapunov equation approach, the so-called robustness is concerned with the design of a filter such that for all admissible uncertainties, the actual fused steady-state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady-state Kalman estimators are proved. An example with application to autoregressive (AR) signal processing is proposed, which shows that the robust fusion signal estimation problems can be solved by the robust fusion state estimation method. Simulation example shows the effectiveness and correctness of the proposed method.
Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

With the rapid development of railway traffic, traffic safety has become a focus.The ZPW-2000A jointless track circuit is an important part of train control systems.Currently, the fault detection of the ZPW-2000A jointless track circuit still relies on the experience of maintenance personnel, which can introduce several problems, such as a low fault detection efficiency and large amounts of required labor.Although some artificial intelligence fault detection algorithms for the ZPW-2000A track circuit have been developed, their detection accuracy is not high enough to meet the needs of large-scale applications, and due to security requirements, the actual ZPW-2000A track circuit fault data cannot be directly obtained in large quantities.To solve these problems, an equivalent theoretical model of the Chinese ZPW-2000A jointless track circuit is proposed by using four-terminal network theory.Through this equivalent theoretical model, the original fault data were collected.Considering that the relationship between fault data and fault types of the ZPW-2000A jointless track circuit is not obvious, a deep belief network was designed to detect the fault modes of the ZPW-2000A jointless track circuit.In order to optimize the deep belief network performance, the particle swarm optimization algorithm optimized by the genetic algorithm (GAPSO) was selected to optimize the deep belief network.The simulation experiments indicated that the optimized deep belief network could achieve a 98.5% fault detection accuracy and a 98.6% F1 Score rate, which showed that the deep belief network optimization by the particle swarm optimization algorithm which was optimized by the genetic algorithm (GAPSO-DBN) model proposed in this paper, had high accuracy and robustness.The results show that it had higher accuracy and robustness than other fault detection methods, and it can greatly improve the level of ZPW-2000A track circuit fault detection in the future.
The multifaceted role of mobile technologies as a strategy to combat COVID-19 pandemic

Abstract The coronavirus 2019 (COVID-19) outbreak in China rapidly spread throughout the world, becoming a threatening pandemic with unprecedented consequences. Mobile technologies have led to a revolution in health and their applicability in the context of COVID-19 is promising. In this commentary, we provide an overview of the role that mobile technologies play in the COVID-19 pandemic context and discuss the main issues associated. Four main domains stood out: health communication, prevention, support and research. Strengthening local surveillance systems, geographic contact tracing, support for clinical practice and data collection of real-time longitudinal data at the population level are some of the main advantages of the applications reported so far. The potential conflict to data privacy urges for discussion on their use in a responsible manner. Along with fair regulation and close monitoring of data collection and process, data anonymisation must be a standard and personal data must be deleted after its usage. Preparation is key for effective containment of a public health crisis and learning lessons on the role of mobile technologies is useful for future challenges in global health. It is noteworthy that their use must be driven by an equitable and inclusive orientation, and mostly integrated into an articulated policy to respond to the crisis.
Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases

Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set.The technique is widely used to generate inferences from a given data set.Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository.EDA can be applied to these standard dataset to generate inferences.In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset.A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation.Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.
Transferring Source Style in Non-Parallel Voice Conversion

Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information. Most VC approaches ignore modeling of the speaking style (e.g. emotion and emphasis), which may contain the factors intentionally added by the speaker and should be retained during conversion. This study proposes a sequence-to-sequence based non-parallel VC approach, which has the capability of transferring the speaking style from the source speech to the converted speech by explicitly modeling. Objective evaluation and subjective listening tests show superiority of the proposed VC approach in terms of speech naturalness and speaker similarity of the converted speech. Experiments are also conducted to show the source-style transferability of the proposed approach.
Optimal Client Sampling for Federated Learning

It is well understood that client-master communication can be a primary bottleneck in Federated Learning. In this work, we address this issue with a novel client subsampling scheme, where we restrict the number of clients allowed to communicate their updates back to the master node. In each communication round, all participating clients compute their updates, but only the ones with "important" updates communicate back to the master. We show that importance can be measured using only the norm of the update and give a formula for optimal client participation. This formula minimizes the distance between the full update, where all clients participate, and our limited update, where the number of participating clients is restricted. In addition, we provide a simple algorithm that approximates the optimal formula for client participation, which only requires secure aggregation and thus does not compromise client privacy. We show both theoretically and empirically that for Distributed SGD (DSGD) and Federated Averaging (FedAvg), the performance of our approach can be close to full participation and superior to the baseline where participating clients are sampled uniformly. Moreover, our approach is orthogonal to and compatible with existing methods for reducing communication overhead, such as local methods and communication compression methods.
Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]

The articles in this special section focus on graph signal processing. Generically, the networks that sustain our societies can be understood as complex systems formed by multiple nodes, where global network behavior arises from local interactions between connected nodes. More succinctly, a network or a graph can be defined as a structure that encodes relationships between pairs of elements of a set. The simplicity of this definition drives the application of graphs and networks to a wide variety of disciplines, such as biology, medicine, psychology, sociology, economics, engineering, computer science, and so on.
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition

In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR training is conducted via minimizing the expected edit distance between the reference label sequence and on-the-fly generated N-best hypothesis. We also introduce a heuristic to incorporate an external neural network language model (NNLM) in RNN-T beam search decoding and explore MBR training with the external NNLM. Experimental results demonstrate an MBR trained model outperforms a RNN-T trained model substantially and further improvements can be achieved if trained with an external NNLM. Our best MBR trained system achieves absolute character error rate (CER) reductions of 1.2% and 0.5% on read and spontaneous Mandarin speech respectively over a strong convolution and transformer based RNN-T baseline trained on ~21,000 hours of speech.
Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence

With the rapid globalisation, enterprises need to strengthen their core capabilities and enhance their competitive advantage. Artificial intelligence could assist companies to manage corporate knowledge and make intelligent decisions. Firstly, this work introduces the foundation of the organisation of the main context of enterprise knowledge management network module. Next, an organisation system of the enterprise knowledge management network module based on multi-agent modelling is constructed. The simulation of system dynamics of knowledge dissemination is studied based on the multi-agent enterprise knowledge management network module organisation system. The results show that the scale of the network, the dispersion degree of knowledge stock is inversely related to the efficiency of knowledge dissemination. The proposed method could assist in evaluating the knowledge transfer efficiency of network-based enterprises and serve as a theoretical basis for efficiency improvement.