Reviewerly/exHarmony huggingface dataset:
Telecardiological COVID-19 (2<sup>nd</sup>) Wave: Metaheuristic-Key Guides Protected Encryption of Heterogeneous Cardiac Reports

The epic COVID-19 had pushed the clinical sciences for another new allied branch as telemedicine services. In the field of COVID-19 (2 nd ) wave telemedicine, Internet and nature propelled algorithms helped to impart private data of various cardiovascular reports to various cardiologists for better treatment, perspectives, and opinion. Such heterogeneous cardiovascular reports are to be gotten so as to re-establish the patients’ protection. Metaheuristic-key has been proposed through metaheuristics calculation followed by the standard AES 128 bits encryption. Cardiovascular infections (CVDs) are heart sickness identified with blockage of arteries and veins. Heart co-morbid patients are at the most elevated danger of COVID-19. Such patients are to be analyzed and treated appropriately within the restrictions of lockdown. This paper presents a got protected directing of the heterogeneous cardiovascular reports of the patients. Such were to be applied on the proposed metaheuristic-key followed by AES encryption. Making the heterogeneous reports into non-meaningful organization for the gatecrashers is the vital target of the proposed method. A few numerical tests were carried on the proposed strategy, and getting worthy outcomes. To translate the proposed metaheuristic-key through quickest figuring computing framework, the measure of time required has been calculated as 8.5 × 10 52 years. Along with these fine lines, pushing the COVID-19 telecardiology framework with more got and remarkable credits on the society.
Quantum-inspired meta-heuristic algorithms with deep learning for facial expression recognition under varying yaw angles

In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90 ∘ , –45 ∘ , 0 ∘ , 45 ∘ , and 90 ∘ . The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.
Analysis and Construction of EEMD Smart Model and Fuzzy Forecasting through Improved Bayesian Estimation

Abstract Different portfolio models will have a great impact on the model rate of return. In order to meet the data needs of portfolio investment forecasting, a big data generation method based on incremental Bayesian network model is proposed. Firstly, the fuzzy interval number is constructed by using the global GM (1pare 1) prediction model, then the fuzzy Mmurv model is established based on the interval fuzzy number, and the model is optimized based on the midpoint, radius and acceptability of the interval number, thus a single objective programming model with parameters is obtained. The future data is partially generated by using the time series generation algorithm, and the Bayesian network model trained by historical data is updated with the newly generated data. So that the updated Bayesian network can reflect the relationship between the variables and the laws contained in the new and old financial data in this period of time. And use the model to predict the amount of foreign direct investment absorbed by China in 2020, test the robustness and analyze the impact of major random events to show the reliability of the prediction. The analysis is carried out on multiple scales. The results show that the integration of IMF1 and IMF2 components through the white noise test can be regarded as the random impact of external factors; the cycle length of IMF3 component is about 3 years, which can be regarded as the inventory cycle caused by the change of enterprise inventory; the cycle length of IMF4 component is 5 years, which can be regarded as political cycle; the cycle length of IMF5 component is about 20 years, which can be regarded as construction cycle. The set of portfolio paths is generated by path search algorithm in Bayesian network, and the big data set with real data characteristics is generated according to the probability distribution of each path. The experimental results show that the method is feasible and ensures a certain accuracy. The forecast results show that the amount of foreign direct investment in China will continue to fluctuate seasonally and in a trend in recent years.
Hardware implementation of Multi-Rate input SoftMax activation function

The SoftMax activation function is a normalized exponential function that is usually used as an activation function of the last layer of a fully connected neural network. The number of neurons in this layer represents the number of classes. The SoftMax activation function is used to normalize the network outputs to a probability distribution over predicted output classes. In this paper, a multi-rate input SoftMax activation function has been designed and built on FPGA. The unit can read 4 or 2 consecutive inputs or one input, every predefined number of cycles. A ROM design has been utilized to determine the exponential part of the function, while the Coordinate Rotation Digital Computer (CORDIC) reciprocal algorithm has been used to calculate the reciprocal of the sum of the input exponential. Hardware multipliers have been used to calculate the SoftMax output. Unit optimization is achieved by pipelining on the input and output stages. The unit can be configured and controlled by an ARM microcontroller as a complete System-on-Chip (SoC) built on Field Programmable Gate Array (FPGA).
Elastic Knowledge Distillation by Learning From Recollection

Model performance can be further improved with the extra guidance apart from the one-hot ground truth. To achieve it, recently proposed recollection-based methods utilize the valuable information contained in the past training history and derive a "recollection" from it to provide data-driven prior to guide the training. In this article, we focus on two fundamental aspects of this method, i.e., recollection construction and recollection utilization. Specifically, to meet the various demands of models with different capacities and at different training periods, we propose to construct a set of recollections with diverse distributions from the same training history. After that, all the recollections collaborate together to provide guidance, which is adaptive to different model capacities, as well as different training periods, according to our similarity-based elastic knowledge distillation (KD) algorithm. Without any external prior to guide the training, our method achieves a significant performance gain and outperforms the methods of the same category, even as well as KD with well-trained teacher. Extensive experiments and further analysis are conducted to demonstrate the effectiveness of our method.
Minimodel of semantic synthesis of Russian sentences

Abstract A formal linguistitic model is presented, which produces, for a given conceptual representation of an extralinguistic situation, a corresponding semantic representation [SemR] that, in its turn, underlies the deep-syntactic representations of four near-synonymous Russian sentences expressing the starting information. Two full-fledged lexical entries are given for the lexemes besporjadki ‘disturbance’ and stolknovenie ‘clash (N) ’, appearing in these sentences. Some principles of lexicalization – that is, matching the formal lexicographic definitions to the starting semantic representation in order to produce the deep-syntactic structures of the corresponding sentences – are formulated and illustrated; the problem of approximate matching is dealt with in sufficient detail.
Embedding Bottleneck Gated Recurrent Unit Network for Radar Signal Recognition

Radar signal recognition plays an significant role in civil applications. Corresponding to two types of intentional modulation signal and unintentional fingerprint signal, radar signal recognition has two kinds of tasks—automatic modulation classification and radar emitter identification. In this paper, we propose a Embedding Bottleneck Gated Recurrent Unit (EBGRU) network that can handle these two tasks separately. The EBGRU consists of three main processing steps. Firstly, the normalized signal pulses are trained in pulse embedding network containing several embedding methods: Pulse2Vec, GloVeP and EPMo, during which we regard the radar signal pulses as radar signal-linguistic sequences for the first time. Then, pulses embeddings are added to original pulses and are sampled to form latent representations of pulses through information bottleneck. Finally, the gated recurrent unit network is utilized to predict radar signal labels. Experiment results show that the proposed method has reached 95.33% on simulated modulation signals and 94.67% at real intercepted emitter signals with relatively less network parameters.
Virtual label expansion-Highlighted key features for few-shot learning

The goal of the few-shot image classification is to identify the category based on a very small number of labeled samples. Two of the key problems are the insufficient amount of labeled data and the unknown category (the inconsistency between the training category and the test category). For these two problems, we propose a new few-shot classification model VE-HKF. First, we introduce a Virtual label expansion mechanism (VE). This mechanism expands the support set data by using the unlabeled data in the query set, thus increasing the number of samples in the support set and making the extracted features more robust. Second, we introduced a Highlighted key features mechanism (HKF). The mechanism first generates a mask through operations such as class average vector and dimensionality reduction and uses this mask to shield and support some irrelevant features, to highlight the important features between samples of each category in a disguised manner, and then focus on the support set and the features of the query set samples to highlight the common features between the support set and the query set, making the extracted features more conducive to classification. On the three data sets of Mini-ImageNet, Omniglot, and Tiered-ImageNet, our model has achieved good results.
Image Enhancement of Low Light UAV via Global Illumination Self-aware feature Estimation

UAV images acquired under low light conditions are often characterized by low contrast and poor visual effect. To improve image quality, a low light UAV image enhancement method via global illumination self-aware feature estimation was proposed. First, a novel lightweight GhostNet is introduced to extract deeper image features. Secondly, the self-aware module is used to correct the possible missing information between encoder network and decoder network. Finally, gradient loss and structural similarity loss are used to constrain the network to achieve the goal of edge preservation and detail restoration. Through extensive experiments, the method proposed can effectively improve the visualization effect, and get more natural and real results.
Configurable In-Database Similarity Search of Electronic Medical Records

With the development of technology, Electronic Medical Re-cords (EMRs) are widely used for medical analysis through methods such as similarity search. Typical EMRs contain attributes of different data types including string, enumeration and numeric data, and are commonly stored in a database. However, many EMR similarity search algorithms neither separate different data types nor conduct search directly in the database. In addition, for researchers and doctors who need similarity search but do not have strong programming background, a user-friendly interface is missing. Therefore, we design a tool "SIR" to solve the aforementioned problems. SIR can conduct configurable similarity search in high dimensions (within 0.0931 and 0.7824 s respectively using its basic and advanced version), can be embedded directly in the database, and has an intuitive interface.
The Role of Location-based Augmented Reality in Enhancing Visit Intention of Cultural Heritage Site

Applications based on Augmented Reality are increasingly adopted by various industries, primarily when supported by smartphones and tablets. The purpose of this study is to examine the role of location-based augmented reality in better presenting a cultural heritage site. To meet the objectives of the study, the authors distributed surveys and conducted interviews (100 respondents from the museum were involved in the inquiry). Descriptive statistics were employed to assess the quantitative descriptions of participants’ responses. The result is the proposed BataviAR application to assist visitors in having informal learning at the historic site of Jakarta Old Town and enhance the tourists’ experience by displaying directions in simplified searching. Although these applications are often little more than experimental prototypes, AR technology is proving highly flexible and is showing great potential in numerous areas and industrial sectors, and they will become even more widespread in the near future.
A Blockchain-Based Scheme of Data Sharing for Housing Provident Fund

This paper proposes a secure, reliable and collaborative data-sharing system for China's housing provident fund based on blockchain. Firstly, federal computing was introduced to realize "available but invisible" sharing of data about housing provident fund, which reduces the data leakage risk and improves the data availability. Secondly, four data sharing modes were proposed to deal with different situations with different amount of data provider and data. Lastly, to realize individual data deep sharing on the premise of security, an enterprise and personal information query authorization mechanism was established to provide solutions to personal and institutional authorization. This system helps to realize both the internal and external data sharing of the housing provident fund system under the premise of security and privacy protection. This system improves the efficiency of housing provident fund issue, and fully taps the value of data comprehensively.
A Data Agent Inspired by Interpersonal Interaction Behaviors for Wireless Sensor Networks

Event monitoring is the main purpose of monitoring applications based on wireless sensor networks (WSNs). To realize the autonomous representation of tunnel disasters in WSNs, this work proposes a data agent (DA) inspired by interpersonal interaction behaviors, which bridges the gap between data and humans. The DA divides event monitoring into four states: 1) active perception; 2) understanding; 3) thinking; and 4) representation, to realize a kind of human-like event monitoring. First, a DA behavior model based on a finite-state machine is proposed, which can adaptively switch among these four states. Second, four methods, including active perception based on a dual neuron perception structure, understanding for forming the information granularity, thinking for forming the disaster knowledge graph, and representation using grayscale and knowledge graphs are proposed to realize the four states of the DA. The experimental results show that the DA inspired by interpersonal interaction can make WSNs not only achieve efficient and accurate disaster self-monitoring but also represent the disaster situation intuitively and quickly through grayscale and knowledge graphs.
Interactive Quantum Classifier Inspired by Quantum Open System Theory

Quantum theory has attracted people's attention since it was proposed. Due to its unique advantages in information storage and processing, quantum information processing has become the most popular research field. Quantum theory also provides us with new methods or concepts for information manipulation and processing. The basic problems of classical physics are basically trying to be solved in a situation of isolation from the surrounding environment to reduce the complexity of the analysis problem, but the quantum system inevitably produces decoherence and establishes a close relationship with the environment such as entanglement, so the formal framework of quantum mechanics is inherently capable of depicting complex relationships. Based on the principle of quantum open system, a classifier under the formal framework of quantum mechanics is established to simulate the evolution process of open systems, that is, the interaction process between the target system and the environment. Specifically, we regard the features (or attributes) of the sample as environmental factors that affect the decision-making of the target system, and the target system can obtain the categories (or labels) of the sample through measurement. Based on this, we use the formal framework of quantum mechanics to establish a more natural and tighter correspondence between attributes and labels. Limited by the limitations of simulating quantum operations on classical computers, we conducted experiments on two lightweight machine learning datasets and compared them with mainstream classification algorithms. Experimental results show that the classification algorithm is better than the comparison models, and it also reveals the potential of the algorithm.
Exercise Abnormality Detection Using BlazePose Skeleton Reconstruction

There still exists a knowledge gap in the field of computer vision in respect of posture prediction and deviation evaluation is an important metric for various medical applications, that require posture abnormality quantization. Our paper proposes a deep heuristic neural network architecture, using BlazePose as a backbone, that is capable of reconstructing users skeleton from a real-time monocular video feed, using which we are able to evaluate the subjects performed exercise and measure the deviation from expected values. The proposed heuristics are able to identify and evaluate most of the abnormalities, with the highest indicator of postural issues being the spinal deviation accounting for 95%. Additional evaluation of real-time performance has shown that our method is capable of maintaining 23-ms response times, making it applicable to real-time applications.
Human and Transformer-Based Prosodic Phrasing in Two Speech Genres

The chief objective of the study was to observe phrasing behaviour of transformer-based neural networks from the linguistic point of view. The transformer-based architecture mapped prosodic phrasing in isolated sentences read out on request, but was commanded to predict prosodic phrases in continuous texts of journalistic style taken from radio news bulletins. The transfer was quite successful in that most of the prosodic phrase boundaries in the actual newsreading (established by expert auditory analysis) were correctly suggested by the machine. This result is not unexpected as both genres belong to clearly enunciated informative speaking style. The outcome partially rehabilitates the so-called laboratory speech, which is sometimes branded as ecologically invalid. The follow-up analyses revealed that the differences between human phrasing in news bulletins and the partition suggested by the machine can be classified into meaningful linguistic categories based on the syntactic structure or semantic contents, and as such, they can inform further research design.
Estimation and Selection for High-Order Markov Chains with Bayesian Mixture Transition Distribution Models

We develop a mixture model and diagnostic for Bayesian estimation and selection in high-order, discrete-state Markov chains. Both extend the mixture transition distribution, which constructs a transition probability tensor by aggregating probabilities from a set of single-lag transition matrices, through inclusion of mixture components dependent on multiple lags. We demonstrate two uses for the proposed model: identification of relevant lags through over-specification and shrinkage via priors for sparse probability vectors, and parsimonious approximation of multi-lag dynamics by mixing low-order transition models. The diagnostic yields a general and interpretable mixture decomposition for transition probability tensors estimated by any means. We demonstrate the utility of the model and diagnostic with simulation studies, and further apply the methodology to a data analysis from the high-order Markov chain literature, and to a time series of pink salmon abundance in Alaska, United States. Supplemental files for this article are available online.
Leveraging Multi-Faceted User Preferences for Improving Click-Through Rate Predictions

Recommender systems have been widely adopted by many firms across various industries over the past decade, as they could provide numerous economic benefits to the industry, such as influencing consumer choices, generating the lift in sales, and enhancing consumer trust. Many currently-deployed recommender systems primiarily optimize the similarity measure following the Collaborative Filtering paradigm during the recommendation performance, which focuses on the matching between targeted users and items of their interests. Although effective and useful in many cases, these still face several important challenges to fully address consumers' concerns. In particular, they usually provide recommendations of the most similar items related to the consumption records, without taking into account the multi-faceted consumer preferences. For example, some consumers would be satisfied to receive recommendations that are novel and unexpected to broaden their horizons, while others might be interested to receive recommendations from other product categories to expand their interests. Falling to do so would lead to the filter bubble and exploration-exploitation trade-off phenomenon, which is undesirable because it would significantly reduce sales diversity and deteriorate user satisfaction with recommender systems.