Reviewerly/exHarmony huggingface dataset:
Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings

Opinion mining has significantly supported knowledge and decision-making. This research analysed the content of online reviews including the text of reviews and their rankings. The restaurant reviews of Yelp website have been analysed into two sentiment classifications, binary classification (positive and negative) and ternary classification (positive, negative, and neutral). Three different types of predictive models have been applied including: machine learning, deep learning and transfer learning models. In addition, we propose a new unsupervised approach to apply for aspect-level sentiment classification based on semantic similarity, which allows our framework to leverage the powerful capacity of pre-trained language models like GloVe and eliminated many of the complications associated with the supervised learning models. Food, service, ambience, and price are the aspects that have been categorized according to their sentiment context. In conclusion, 98.30% of the maximum accuracy obtained using ALBERT model. The proposed aspect extraction method achieved an accuracy of 83.04%.
A survey on population-based meta-heuristic algorithms for motion planning of aircraft

Meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.
Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation

An algorithm that uses quantum machine learning methods to estimate the ground state energy of any parametrization of the Hamiltonian within a certain trusted region is developed, reducing the total computational cost of these variational quantum algorithms.
Continuous Multimodal Biometric Authentication Schemes: A Systematic Review

Building safeguards against illegitimate access and authentication is a cornerstone for securing systems. Existing user authentication schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. A range of research proposals have suggested continuous multimodal biometric authentication (CMBA) systems as a reliable solution. Though contemporary authentication systems have the potential to change their current authentication scheme, there is a lack of critical analysis of current progress in the field to foster and influence practical solutions. This paper provides a systematic survey of existing literature on CMBA systems, followed by analysis to identify and discuss current research and future trends. The study has found that many diverse biometric characteristics are used for multimodal biometric authentication systems. The majority of the studies in the literature reviewed apply supervised learning approaches as a classification technique, and score level fusion is predominantly used as a fusion model. The review has determined however that there is a lack of comparative analysis on CMBA design in terms of combinations of biometric types (behavioural only, physiological only, or both), machine learning algorithms (unsupervised learning and semi-supervised learning), and fusion models. Most of the studies evaluated a CMBA system’s accuracy functionality, such as False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). However, security, scalability and usability (user acceptance and satisfaction) are generally not addressed thoroughly even though these are key factors for system success in a real deployment. Furthermore, a CMBA system should be implemented and evaluated extensively on real data without restriction to prove that such systems are feasible.
WITHDRAWN: A systematic study of sentiment analysis for social media data

Sentiment Analysis is considered as the technique of defining and extracting human feelings through the unstructured text and is done through Natural Language Processing and Machine Learning. Machine Learning is a great way that helps in learning and training the datasets obtained from social media. It is the most ubiquitous strategy used in sentiment analysis. With machine learning, the lexicon-based, as well as the rule-based methods, can also be utilized. Analysis of sentiment is the best instrument to determine whether the evaluation is positive or negative. The various complications in the assessment of thoughts are one that the public does not always articulate feelings, in the same manner, which means a few expresses in the mode of providing scores and remarks and others in the form of phrases that do not convey any proper mindsets. The reviews are classified as positive, negative, and neutral. This review provides a synthesis of various prevailing sentiment analysis techniques employed on social media data. Different studies conducted in this area of research will be explored while discussing the algorithms developed. These include Naïve Bayes, K-nearest method, Random Forest, Support Vector Machine, and Deep Learning. All these classification techniques were analyzed, with their issues and challenges.
Harmonic Feature Activation for Few-Shot Semantic Segmentation

Few-shot semantic segmentation remains an open problem because limited support (training) images are insufficient to represent the diverse semantics within target categories. Conventional methods typically model a target category solely using information from the support image(s), resulting in incomplete semantic activation. In this paper, we propose a novel few-shot segmentation approach, termed harmonic feature activation (HFA), with the aim to implement dense support-to-query semantic transform by incorporating the features of both query and support images. HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way. HFA incorporates a low-rank decomposition procedure, which speeds up bilinear feature activation with negligible performance cost. In addition, a semantic diffusion procedure is fused with HFA, which further improves the global harmony and local consistency of the feature activation. Extensive experiments on commonly used datasets (PASCAL VOC and MS COCO) show that HFA improves the state-of-the-arts with significant margins. Code is available at https://github.com/Bibikiller/HFA.
Mind the GAP: Security &amp; Privacy Risks of Contact Tracing Apps

Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called “Google/Apple Proposal”, which we abbreviate by “GAP”. We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.
How to Represent Part-Whole Hierarchies in a Neural Network

This article does not describe a working system. Instead, it presents a single idea about representation that allows advances made by several different groups to be combined into an imaginary system called GLOM.1 The advances include transformers, neural fields, contrastive representation learning, distillation, and capsules. GLOM answers the question: How can a neural network with a fixed architecture parse an image into a part-whole hierarchy that has a different structure for each image? The idea is simply to use islands of identical vectors to represent the nodes in the parse tree. If GLOM can be made to work, it should significantly improve the interpretability of the representations produced by transformer-like systems when applied to vision or language.
Assessing the hotel service robot interaction on tourists' behaviour: the role of anthropomorphism

Purpose The main purpose of this study is to investigate the impact of service robots on hotel visitors' behaviour and to verify the role of anthropomorphism(human likeness) in customer satisfaction with robots. Design/methodology/approach An online survey of 381 respondents was conducted, divided into three types of robots according to the level of anthropomorphism. The research model was thoroughly tested using the PLS-SEM method. Research model was tested thoroughly using the PLS-SEM method. Findings This study found that user satisfaction with service robots in a hotel had a positive impact on user satisfaction, attitude towards the hotel and room purchase intention. Moreover, our results showed that users were most likely to accept medium-human likeness robots and least likely to accept high–human likeness robots. Originality/value This study proposes influencing factors to be considered when researching hotel service robots, as well as practical suggestions for any hotel intending to use or currently using a service robot.
Blockchain for 5G Healthcare architecture

Technology does not stand still. Modern technologies are being introduced in all spheres of our life, including the healthcare system. In this article, we examined the prospects for introducing 5G technology and blockchain into the healthcare system. From a technical point of view, we analyzed the key weak points in the healthcare system, analyzed the capabilities and advantages of 5G, blockchain and related technologies, considered the benefits of their integration with the healthcare system. Based on the results obtained, we proposed a blockchain-based 5G healthcare architecture.
Research on a Covert Communication Model Realized by Using Smart Contracts in Blockchain Environment

The traditional covert communication channel relying on a third-party node is vulnerable to attack. The data are easily tampered with and the identity information of the communication party is fragile. Blockchain has the characteristics of decentralization and tamper resistance, which can effectively solve the above problems. In addition, some confidential information needs to be transmitted covertly in the transparent blockchain. A smart contract deployed in the blockchain to automatically realize its function can replace a centralized node to provide credible guarantee for communication. The diversity of parameters, data redundancy, and code programmability of smart contract make it an excellent carrier for covert communication under blockchain. In this article, we propose a covert communication model combined with smart contracts to covertly transfer information in the blockchain environment. To implement this model, we use the parameters in the contract to map the secret information sequence, and call the contract to transfer message. Voting contract and secret bidding contract are combined to instantiate the proposed model, and optimized versions of the two contracts are also proposed to reduce costs. Moreover, we use encryption algorithms and two-round protocols to ensure data privacy and design corresponding information embedding and transmission methods for different scenarios. To improve the concealment of communication, redundant options, effective price ranges, and invalid bids are set in two contracts, respectively. The experimental results show that the proposed model has tamper resistance and low complexity, and it is feasible to use this model for covert communication.
Towards a Blockchain Assisted Patient Owned System for Electronic Health Records

Security and privacy of patients’ data is a major concern in the healthcare industry. In this paper, we propose a system that activates robust security and privacy of patients’ medical records as well as enables interoperability and data exchange between the different healthcare providers. The work proposes the shift from patient’s electronic health records being managed and controlled by the healthcare industry to a patient-centric application where patients are in control of their data. The aim of this research is to build an Electronic Healthcare Record (EHR) system that is layered on the Ethereum blockchain platform and smart contract in order to eliminate the need for third-party systems. With this system, the healthcare provider can search for patient’s data and request the patients’ consent to access it. Patients manage their data which enables an expedited data exchange across EHR systems. Each patient’s data are stored on the peer-to-peer node ledger. The proposed patient-centric EHR platform is cross-platform compliant, as it can be accessed via personal computers and mobile devices and facilitates interoperability across healthcare providers as patients’ medical records are gathered from different healthcare providers and stored in a unified format. The proposed framework is tested on a private Ethereum network using Ganache. The results show the effectiveness of the system with respect to security, privacy, performance and interoperability.
Feature construction and smote-based imbalance handling for multi-label learning

The class-imbalance is intrinsic in Multi-label datasets due to the higher number of labels, few relevant labels in many instances, and a varied number of relevant instances for different labels. It causes multi-label learning biased toward majority instances for many labels. Therefore, it is essential to handle the multi-label datasets’ imbalances before using any multi-label learning algorithm. There are a handful of proposals in recent times to extricate this problem. However, it is still a significant challenge to date. This paper proposes a method termed Feature Construction and Smote-based Imbalance handling (FCSMI) for multi-label datasets. The FCSMI works in a label-wise manner as follows. First, it determines whether the label is a minority based on the mean imbalance ratio. Further, for minority labels, it calculates the distances of each instance from all the minority-instances. These distances are used as features. Then, it uses Smote to balance the ratio between minority and majority instances. Finally, the dataset, which has a lower imbalance ratio than its initial counterpart, is used to train the classifier. The experimental results demonstrate the effectiveness of our proposed FCSMI method compared to the prevailing state-of-the-art multi-label sampling methods.
Authentication and Key Management in Distributed IoT Using Blockchain Technology

The exponential growth in the number of connected devices as well as the data produced from these devices call for a secure and efficient access control mechanism that can ensure the privacy of both users and data.Most of the conventional key management mechanisms depend upon a trusted third party like a registration center or key generation center for the generation and management of keys.Trusting a third party has its own ramifications and results in a centralized architecture; therefore this article addresses these issues by designing a Blockchain based distributed IoT architecture that uses Hash Chains for secure key management.The proposed architecture exploits the key characteristics of Blockchain technology such as openness, immutability, traceability, and fault tolerance, to ensure data privacy in IoT scenarios and, thus, provides a secure environment for communication.The paper also proposes a scheme for secure and efficient key generation and management for mutual authentication between communication entities.The proposed scheme uses a one-way hash chain technique to provide a set of public and private key pairs to the IoT devices that allow the key pairs to verify themselves at any time.Experimental analysis confirms the superior performance of the proposed scheme to the conventional mechanisms.
Controllable Emotion Transfer For End-to-End Speech Synthesis

Emotion embedding space learned from references is a straight-forward approach for emotion transfer in encoder-decoder structured emotional text to speech (TTS) systems. However, the transferred emotion in the synthetic speech is not accurate and expressive enough with emotion category confusions. Moreover, it is hard to select an appropriate reference to deliver desired emotion strength. To solve these problems, we propose a novel approach based on Tacotron. First, we plug two emotion classifiers - one after the reference encoder, one after the decoder output - to enhance the emotion-discriminative ability of the emotion embedding and the predicted mel-spectrum. Second, we adopt style loss to measure the difference between the generated and reference mel-spectrum. The emotion strength in the synthetic speech can be controlled by adjusting the value of the emotion embedding as the emotion embedding can be viewed as the feature map of the mel-spectrum. Experiments on emotion transfer and strength control have shown that the synthetic speech of the proposed method is more accurate and expressive with less emotion category confusions and the control of emotion strength is more salient to listeners.
Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.
Multimodal feature-wise co-attention method for visual question answering

VQA attracts lots of researchers in recent years. It could be potentially applied to the remote consultation of COVID-19. Attention mechanisms provide an effective way of utilizing visual and question information selectively in visual question and answering (VQA). The attention methods of existing VQA models generally focus on spatial dimension. In other words, the attention is modeled as spatial probabilities that re-weights the image region or word token features. However, feature-wise attention cannot be ignored, as image and question representations are organized in both spatial and feature-wise modes. Taking the question “What is the color of the woman’s hair” for example, identifying the hair color attribute feature is as important as focusing on the hair region. In this paper, we propose a novel neural network module named “multimodal feature-wise attention module” (MulFA) to model the feature-wise attention. Extensive experiments show that MulFA is capable of filtering representations for feature refinement and leads to improved performance. By introducing MulFA modules, we construct an effective union feature-wise and spatial co-attention network (UFSCAN) model for VQA. Our evaluation on two large-scale VQA datasets, VQA 1.0 and VQA 2.0, shows that UFSCAN achieves performance competitive with state-of-the-art models.
A reference-free underwater image quality assessment metric in frequency domain

Owing to the complexity of the underwater environment and the limitations of imaging devices, the quality of underwater images varies differently, which may affect the practical applications in modern military, scientific research, and other fields. Thus, achieving subjective quality assessment to distinguish different qualities of underwater images has an important guiding role for subsequent tasks. In this paper, considering the underwater image degradation effect and human visual perception scheme, an effective reference-free underwater image quality assessment metric is designed by combining the colorfulness, contrast, and sharpness cues. Specifically, inspired by the different sensibility of humans to high-frequency and low-frequency information, we design a more comprehensive color measurement in spatial domain and frequency domain. In addition, for the low contrast caused by the backward scattering, we propose a dark channel prior weighted contrast measure to enhance the discrimination ability of the original contrast measurement. The sharpness measurement is used to evaluate the blur effect caused by the forward scattering of the underwater image. Finally, these three measurements are combined by the weighted summation, where the weighed coefficients are obtained by multiple linear regression. Moreover, we collect a large dataset for underwater image quality assessment for testing and evaluating different methods. Experiments on this dataset demonstrate the superior performance both qualitatively and quantitatively.