Reviewerly/exHarmony huggingface dataset:
User Acceptance of Hotel Service Robots Using the Quantitative Kano Model

With today’s rapid technological developments, many have applied artificial intelligence and robot technology to the tourism and hotel industries, with hotel service robots (HSRs) being gradually developed. At present, more technology development companies have focused their attention on improving HSRs’ different attributes to improve their acceptance by users, thereby enhancing market competitiveness and improving customer loyalty. Understanding consumer acceptance of HSRs is important. Based on a literature review of the user’s acceptance of HSR attributes and HSRs’ current development status, some factors and attributes were extracted. For the questionnaire’s design and data extraction, the quantitative Kano model was used. The data obtained were compiled and analyzed using Microsoft Excel and SPSS. This study aims to (1) qualitatively apply the perceived value theory to develop specific HSR attributes and (2) quantitatively examine these attributes concerning public acceptance. By integrating the Kano model with the perceived value theory, this study provides empirical evidence of a nonlinear relationship between HSRs’ perceived value and user acceptance by exploring various attributes affecting the user’s acceptance of HSRs and the degree of change brought by the different attributes. The research result reveals the multidimensional impacts of perceived value, prompting users to embrace newer HSR technologies.
Pursuit and Evasion Strategy of a Differential Game Based on Deep Reinforcement Learning

Since the emergence of deep neural network (DNN), it has achieved excellent performance in various research areas. As the combination of DNN and reinforcement learning, deep reinforcement learning (DRL) becomes a new paradigm for solving differential game problems. In this study, we build up a reinforcement learning environment and apply relevant DRL methods to a specific bio-inspired differential game problem: the dog sheep game. The dog sheep game environment is set on a circle where the dog chases down the sheep attempting to escape. According to some presuppositions, we are able to acquire the kinematic pursuit and evasion strategy. Next, this study implements the value-based deep Q network (DQN) model and the deep deterministic policy gradient (DDPG) model to the dog sheep game, attempting to endow the sheep the ability to escape successfully. To enhance the performance of the DQN model, this study brought up the reward mechanism with a time-out strategy and the game environment with an attenuation mechanism of the steering angle of sheep. These modifications effectively increase the probability of escape for the sheep. Furthermore, the DDPG model is adopted due to its continuous action space. Results show the modifications of the DQN model effectively increase the escape probabilities to the same level as the DDPG model. When it comes to the learning ability under various environment difficulties, the refined DQN and the DDPG models have bigger performance enhancement over the naive evasion model in harsh environments than in loose environments.
Improving the Pap test with artificial intelligence

The cytology community should be excited about the emergence of artificial intelligence–based solutions and embrace these promising technologies. However, there is also a need to determine how best to adopt these tools into routine practice and to monitor their long‐term usefulness.
OFES: Optimal feature evaluation and selection for multi-class classification

The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. Feature evaluation and selection are critical steps to decide a small set of high-quality features to build accurate and efficient classifiers since low-quality features not only have negative impacts on classification results but also increase the complexity of classification algorithms. Current popular feature selection algorithms are not sufficient in selecting a set of high-quality features and discarding low-quality features, especially for streaming data. This paper proposes a novel and efficient approach, optimal feature evaluation and selection (OFES), to evaluate and select high-quality features for multi-class classification. OFES first measures the difference between any two classes based on the feature that is to be evaluated. Then, it defines two quantitative measures to evaluate quality of the feature and identify high-quality features. Applying OFES in a multi-class classification application that identifies users based on their arm movement patterns, we find when compared with other popular feature evaluation and selection approaches, such as Information Gain Feature Ranking and Random Projections with Matlab feature ranking, OFES identifies a set of high-quality features that improves the accuracy of classification regardless of different classification algorithms. It also demonstrates great scalability with the increase of number of classes and yields a higher accuracy of 95%.
Cryptocurrencies and Tokens Lifetime Analysis from 2009 to 2021

The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest.
Learning temporal information and object relation for zero-shot action recognition

It is challenging to achieve zero-shot action recognition. Current approaches utilize the names or classification scores of the detected objects to model object relations in images, and the recognition performances highly rely on the accuracy of object classification. In fact, humans have the capability to infer unseen action categories using visual knowledge such as motion patterns and object relations. In this work, a novel model is proposed for zero-shot action recognition, which jointly captures object relations of one static frame and models temporal motion patterns of adjacent frames. Specifically, an object detector first detects and extracts object features. Then graph convolutions are conducted to effectively leverage the relations of objects. Meanwhile, three-dimensional convolutional neural networks are adopted to model temporal information. Finally, the above two outputs are separately fed into visual-to-semantic modules to project the visual features into the semantic space. Moreover, a prior knowledge learning method is devised to introduce visual commonsense knowledge with the help of an external dataset. Extensive experiments are conducted on three benchmark datasets of Olympic Sports, HMDB51, and UCF101 to demonstrate the superiority of the proposed model compared to the state-of-the-art methods. • A novel model is proposed for zero-shot action recognition with one branch capturing the relations of objects in static frames and the other branch modeling the temporal motion patterns of adjacent frames. • The relation branch is designed to utilize visual information to construct graphs, which is different from previous methods that necessarily identify the types of observed objects or utilize classification scores. • A visual prior-knowledge learning method is devised to introduce visual commonsense knowledge for the accurate identification of unseen action classes.
KM-MIC: An improved maximum information coefficient based on K-Medoids clustering

In order to measure whether and how things are related, statistical correlation analysis comes into being. Among them, Pearson coefficients, Spearman and Kendall coefficients are widely used, but these correlation analysis methods cannot detect a wide range of relationship types due to their own limitations. Therefore, in 2011, Reshef et al. introduced a new correlation analysis method, maximal information coefficient (MIC), but this method has low computational efficiency and does not give an optimal data division method. On this basis, we propose the maximum information coefficient based on K-Medoids clustering (KM-MIC), which combines the K-Medoids clustering algorithm to optimize the way of data partitioning, and can quickly calculate whether there is correlation between the data. And this method has two major characteristics, generality and equitability.
Construction of an Assessment System for Business English Linguistics Based on RNN Multidimensional Models

The current teaching evaluation system of the course “Introduction to Business English Linguistics” has many problems and should be reformed according to the requirements of the relevant professional syllabus. This paper proposes a recurrent neural network (RNN) modelling technique, which has achieved good results in modelling business English language models. The RNN modelling method is applied to Chinese language modelling according to the characteristics of Chinese language, and the advantages of the two models are combined to propose a fusion model. The experimental results demonstrate that this paper focuses on the process, diversity, and humanity of teaching evaluation and discusses the construction of the evaluation system model of the course “Introduction to Business English Linguistics” in terms of the content and criteria of teaching evaluation and the methods of teaching evaluation, so as to provide a new model for the teaching reform of the course “Introduction to Business English Linguistics”.
On the Reliability of CNNs in Clinical Practice: A Computer-Aided Diagnosis System Case Study

Leukocytes classification is essential to assess their number and status since they are the body’s first defence against infection and disease. Automation of the process can reduce the laborious manual process of review and diagnosis by operators and has been the subject of study for at least two decades. Most computer-aided systems exploit convolutional neural networks for classification purposes without any intermediate step to produce an accurate classification. This work explores the current limitations of deep learning-based methods applied to medical blood smear data. In particular, we consider leukocyte analysis oriented towards leukaemia prediction as a case study. In particular, we aim to demonstrate that a single classification step can undoubtedly lead to incorrect predictions or, worse, to correct predictions obtained with wrong indicators provided by the images. By generating new synthetic leukocyte data, it is possible to demonstrate that the inclusion of a fine-grained method, such as detection or segmentation, before classification is essential to allow the network to understand the adequate information on individual white blood cells correctly. The effectiveness of this study is thoroughly analysed and quantified through a series of experiments on a public data set of blood smears taken under a microscope. Experimental results show that residual networks perform statistically better in this scenario, even though they make correct predictions with incorrect information.
A Comparative Study on Academic Achievement of Mathematics and English with Other Subjects of Secondary Level in BTR of Assam, India, Using Mahalanobis Distance

The present work is a comparative study of four different subjects’ achievements such as mathematics, English, general sciences, and social studies between two student groups in the Bodoland Territorial Region (BTR) in Assam, India. Three different types of groups are considered viz., tribal and nontribal, boys and girls, and rural and urban from different secondary and higher schools of BTR. Mahalanobis Distance (MD) is used to calculate the difference in dynamical nature of achievement between two groups of students in four subjects. The result revealed that there is no significant difference between boys and girls; students from tribal and nontribal communities, students belonging to the rural and urban schools, tribal boys and nontribal boys, and tribal girls and nontribal girls in the dynamical character of achievement of different groups are taken into account.
An intelligent decision support system for the accurate diagnosis of cervical cancer

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. In this paper, we present an intelligent decision support system for the diagnosis of cervical cancer using risk factors outlined in a publicly available dataset. The dataset contains a large imbalance between positive and negative instances. Although sampling techniques may be utilized to address this, due to the high level of imbalance, oversampling or undersampling alone is insufficient to create an adequate balance between the classes, which is crucial for appropriate diagnosis. Hence, we propose a novel resampling technique that hybridizes oversampling and undersampling to induce a proper balance between the two classes. The hybrid strategy ensures that neither the majority class nor the minority class suffers from a reduction in performance or gets overfitted, as would be the case if oversampling or undersampling were used unilaterally. To further enhance the performance of the classifiers, Genetic Algorithm (GA) is applied to identify the key risk factors for cervical cancer diagnosis. Using the optimized feature set of only 8 features out of 32 procured by GA, the Random Forest classifier provided the maximum G-mean score of 94.47%, along with a sensitivity and specificity of 94.25% and 94.69%, respectively. Thus, our proposed hybrid resampling strategy effectively addresses class imbalance, while GA identifies the most important features to maximize the class separation, and the combination of the two provides the best possible performance for the diagnosis of cervical cancer.
Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.
Iris Image Compression Using Deep Convolutional Neural Networks

Compression is a way of encoding digital data so that it takes up less storage and requires less network bandwidth to be transmitted, which is currently an imperative need for iris recognition systems due to the large amounts of data involved, while deep neural networks trained as image auto-encoders have recently emerged a promising direction for advancing the state-of-the-art in image compression, yet the generalizability of these schemes to preserve the unique biometric traits has been questioned when utilized in the corresponding recognition systems. For the first time, we thoroughly investigate the compression effectiveness of DSSLIC, a deep-learning-based image compression model specifically well suited for iris data compression, along with an additional deep-learning based lossy image compression technique. In particular, we relate Full-Reference image quality as measured in terms of Multi-scale Structural Similarity Index (MS-SSIM) and Local Feature Based Visual Security (LFBVS), as well as No-Reference images quality as measured in terms of the Blind Reference-less Image Spatial Quality Evaluator (BRISQUE), to the recognition scores as obtained by a set of concrete recognition systems. We further compare the DSSLIC model performance against several state-of-the-art (non-learning-based) lossy image compression techniques including: the ISO standard JPEG2000, JPEG, H.265 derivate BPG, HEVC, VCC, and AV1 to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression and promising recognition performance of the model over all other techniques on different iris databases.
Investigating the Influence of Feature Sources for Malicious Website Detection

Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites in order to trick users into trusting them. These websites, often a primary method for credential collection, pose a severe threat to large enterprises. Credential collection enables malicious actors to infiltrate enterprise systems without triggering the usual alarms. Therefore, there is a vital need to gain deep insights into the statistical features of these websites that enable Machine Learning (ML) models to classify them from their benign counterparts. Our objective in this paper is to provide this necessary investigation, more specifically, our contribution is to observe and evaluate combinations of feature sources that have not been studied in the existing literature—primarily involving embeddings extracted with Transformer-type neural networks. The second contribution is a new dataset for this problem, GAWAIN, constructed in a way that offers other researchers not only access to data, but our whole data acquisition and processing pipeline. The experiments on our new GAWAIN dataset show that the classification problem is much harder than reported in other studies—we are able to obtain around 84% in terms of test accuracy. For individual feature contributions, the most relevant ones are coming from URL embeddings, indicating that this additional step in the processing pipeline is needed in order to improve predictions. A surprising outcome of the investigation is lack of content-related features (HTML, JavaScript) from the top-10 list. When comparing the prediction outcomes between models trained on commonly used features in the literature versus embedding-related features, the gain with embeddings is slightly above 1% in terms of test accuracy. However, we argue that even this somewhat small increase can play a significant role in detecting malicious websites, and thus these types of feature categories are worth investigating further.
People-centered distributed ledger technology-IoT architectures: A systematic literature review

To understand how distributed ledger technology (DLT) enables people-centered IoT solutions we conducted a systematic literature review of tested implementations since 2017. We created a people-centered classification to analyze 39 implementations. We found that people-centered DLT-IoT architectures are in their infancy and detected no evidence of emerging patterns. We observed that Ethereum is the most used DLT. Fit-for-purpose technologies like IOTA and concepts like Self-Sovereign Identity (SSI) were underrepresented. We noted an increased interest in privacy-preserving and edge-computing mechanisms, and identified three areas for future research. We hope this survey will assist others learning more about people-centered IoT solutions.
Joint diagonalization for a pair of Hermitian quaternion matrices and applications to color face recognition

A new joint diagonalization algorithm for a pair of Hermitian quaternion matrices is derived incorporating real structure-preserving strategy. The structure-preserving joint diagonalization algorithm leads to a novel two-dimensional quaternion linear discriminant analysis (2D-QLDA) method for color face recognition and image reconstruction. 2D-QLDA is mathematically characterized by Hermitian quaternion generalized eigenvalue problem. A weighted norm is obtained as a new measurement to determine the distances among Fisher feature matrices, which helps us avoid generating projected images explicitly. Numerical results based on the real face databases indicate that 2D-QLDA performs better than other 2D-LDA-like methods in color face recognition and is effective in image reconstruction.
A controllable privacy data transmission mechanism for Internet of things system based on blockchain

With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.