Reviewerly/exHarmony huggingface dataset:
Synthetic News as a Tool for Evaluating Urban Area Development Policies

The actions of people and various infrastructural factors are the sources of events that characterize the state of the human life activity environment. The reaction of citizens to such events can carry a semantic load, reflecting the state of affairs and the necessary measures to prevent the development of negative consequences. The analysis of this kind of social response can actually be the basis for one of the aspects of monitoring urban development. Such a source of information requires a study of its specificity. The main form of social response in the modern information space is news feeds of various information resources, because they contain tools for social response to ongoing events in the form of comments and emoji marking. This article describes a study aimed at developing an approach to generating a news feed using heterogeneous sources of information. Approaches to the creation of synthetic news messages that characterize the state of the urban environment and the events taking place in it, based on the parsing of real news, changes in geographic data in real news, text generation using the GPT-3 neural network, text generation based on the templating library and generation of game news based on UrbanStreetWars application are considered. The general principle of forming a single news feed based on a combination of real and synthetic news flow, as well as an algorithm for generating an integrated news flow, is proposed. The results of work the software solution based on the proposed approach are demonstrated. The issue of producing and analyzing the reaction of the urban community to the generated synthetic news flow is considered in the discussion. The results of this study will be useful in decision support systems to analyze the social response to various options for ongoing urban transformation policies.
Blockchain-based Approach to thwart Replay Attacks targeting Remote Keyless Entry Systems

Remote Keyless Entry (RKE) is a revolutionary technology that allows drivers to gain access to their vehicles using a wireless key fob operating on short-range radio waves. This technology offers numerous advantages for drivers, but it is still vulnerable to serious security threats which target the interactions between the fob and car, which may eventually result in car theft. In this paper, we propose a blockchain-based approach to thwart replay attacks targeting cars equipped with RKE systems. We consider the key fob and the car as two separate users of a private blockchain in which a miner authenticates the key fob with the corresponding car to grant or deny access using a smart contract. We validate our findings by a performance evaluation of the time taken by the miner to validate the access to the vehicle.
Exploring the Impact of an Augmented Reality Application for Bespoke Musical Instruments

Bespoke products have gained utmost importance among shoppers in recent years. This paper presents an Augmented Reality (AR) application that can be used to market bespoke musical products such as drums. The AR application allows the users to experience a visual representation of custom-made musical drums through the help of their mobile phones. The AR application enables users to design, modify and review bespoke musical drums without visiting a musical instruments workshop. They could also communicate the alterations in real-time, saving them money and time. The AR functionality allows the users to view their bespoke musical drums in their rooms or studios to visualise how the drums will look once they are designed. The AR application was tested by 25 participants, who used it to create their bespoke solutions. The System Usability Scale (SUS) and Confirmatory Factor Analysis showed that the AR application received positive user feedback and can potentially aid the online personalised shopping experience.
Integrating mobile learning and SPOC‐based flipped classroom to teach a course in water supply and drainage science and engineering

Abstract To promote the combination of mobile learning (m‐learning) in new courses contexts and improve students' learning outcomes, an integrated method of m‐learning and small private online course (SPOC)‐based flipped classroom was proposed and used for teaching the “hydrology” course at Xi'an University of Architecture and Technology (XAUAT). The offering of the new methodology was composed of preclass (knowledge acquisition), in‐class (knowledge internalization), and after‐class (knowledge application) stages, and the WeChat applet Mu classroom with m‐learning technology was adopted in the in‐class stage. To improve the teacher–student interactions and communication, several interactive activities were designed based on Mu classroom, including random roll calls, open questions, objective questions, discussion, and extended discussion. After using the integrated method, the average score of each final exam was improved significantly ( p &lt; .05), and the failed (&lt;60) percentage was decreased significantly ( p = .002 &lt; .01). Positive correlation between the scores of in‐class quizzes and discussion and final exam were obtained ( R 2 = .530, p &lt; .001), indicating that students' outcomes were greatly affected by mobile learning. Moreover, positive feedback from students' questionnaires confirmed that the integrated approach was effective and could motivate students' learning interests and knowledge understanding.
Multi-Strategy Enhanced Harris Hawks Optimization for Global Optimization and Deep Learning-Based Channel Estimation Problems

Harris Hawks Optimization (HHO) simulates the cooperative hunting behavior of Harris hawks and it has the advantages of fewer control parameters, simple principles, and excellent exploitation ability. However, HHO also has the disadvantages of slow convergence and easy falling into local optimality. Aiming at the above shortcomings, this paper proposes a Multi-strategy Enhanced Harris Hawks Optimization (MEHHO). Firstly, the map-compass operator and Cauchy mutation strategy are used to increase the population diversity and improve the ability of the algorithm to jump out of the local optimal. Secondly, a spiral motion strategy is introduced to improve the exploration phase to enhance search efficiency. Finally, the convergence speed and accuracy of the algorithm are improved by greedy selection to fully retain the dominant individuals. The global search capability of the proposed MEHHO is verified by 28 benchmark test functions, and then the parameters of the deep learning network used for channel estimation are optimized by using the MEHHO to verify the practicability of the MEHHO. Experimental results show that the proposed MEHHO has more advantages in solving global optimization problems and improving the accuracy of the channel estimation method based on deep learning.
Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.
E2H Distance-Weighted Minimum Reference Set for Numerical and Categorical Mixture Data and a Bayesian Swap Feature Selection Algorithm

Generally, when developing classification models using supervised learning methods (e.g., support vector machine, neural network, and decision tree), feature selection, as a pre-processing step, is essential to reduce calculation costs and improve the generalization scores. In this regard, the minimum reference set (MRS), which is a feature selection algorithm, can be used. The original MRS considers a feature subset as effective if it leads to the correct classification of all samples by using the 1-nearest neighbor algorithm based on small samples. However, the original MRS is only applicable to numerical features, and the distances between different classes cannot be considered. Therefore, herein, we propose a novel feature subset evaluation algorithm, referred to as the “E2H distance-weighted MRS,” which can be used for a mixture of numerical and categorical features and considers the distances between different classes in the evaluation. Moreover, a Bayesian swap feature selection algorithm, which is used to identify an effective feature subset, is also proposed. The effectiveness of the proposed methods is verified based on experiments conducted using artificially generated data comprising a mixture of numerical and categorical features.
Language use in online support provision: the impact of others’ supportive messages, support-seeking strategy, and channel publicness

Abstract People benefit from high-quality supportive messages online, yet the production of these messages is a complex process that is shaped by multiple factors. As informed by the lens model, this study seeks to investigate the interplay of third party’s supportive messages as a form of behavioral residue and the support seeker’s seeking strategy as an identity claim on providers’ language use in supportive messages conveyed in public and private channels online. Data showed that providers’ use of words that differentiate supportive messages of varying quality (i.e., first-person singular pronouns, second-person pronouns, social process words, cognitive process words, and anxiety words) was influenced by the quality and quantity of others’ comments, the support-seeking strategy, and the publicness of the communication channel in an interactive way. Findings extend the literature by providing a more comprehensive account of online supportive communication and illuminating the actual language used to support others.
Typed–Untyped Interactions: A Comparative Analysis

The literature presents many strategies for enforcing the integrity of types when typed code interacts with untyped code. This article presents a uniform evaluation framework that characterizes the differences among some major existing semantics for typed–untyped interaction. Type system designers can use this framework to analyze the guarantees of their own dynamic semantics.
Discrete Wavelet Transform-Based CNN for Breast Cancer Classification from Histopathology Images

Breast cancer has the highest mortality rate among cancers in women, but its diagnosis is limited. In recent years, there have been many works applying deep learning to computer-aided diagnosis of breast cancer histopathology images. However, the existing methods focus only on the spatial domain approach and the extracted feature information is not comprehensive enough. Furthermore, to reduce the computational burden, they usually crop images into smaller-sized patches resulting in the loss of a large amount of feature information. To address the above issues, we propose a frequency domain learning method based on discrete wavelet transform and convolutional neural network (CNN) for the classification task of breast cancer histopathology images. The method first extracts the feature information in the time and frequency domains using the wavelet transform, while taking into account the different information contained in the three color components of RGB images. Then, the low-frequency information from the decomposition of different color components is then fused and inputs to the CNN for training, meanwhile, other non-critical information is filtered out to improve the computational efficiency. The accuracy of our method is 99.50%, 99.20%, 99.00% and 99.08% for the binary classification task and 95.79%, 92.91%, 90.83% and 93.37% for the multi-classification task, respectively, on the BreakHis dataset.
Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing.
Path Planning Algorithm Based on A_star Algorithm and Q-Learning Algorithm

The path planning algorithm is one of the most important algorithms in indoor mobile robot applications. As an integral part of ground mobile robot research, the path planning problem has greater research and application value. Based on machine learning, the mobile robot is continuously tried and trained in the simulation environment to eventually achieve the optimal path planning requirements for real-time obstacle avoidance, resulting in a new path planning algorithm. To make the planning goal smoother, after optimizing the global path planning A_star algorithm, it is necessary to combine the Q-learning algorithm, so this paper proposes the HA-Q algorithm. Under the HA-Q algorithm, the mobile robot can smoothly move from the specified starting point to the target point where the specified function is designated, to realize the functions of obstacle avoidance and path selection. After some simulation experiments, the HA-Q algorithm is more consistent with the ground mobile robot movement in the actual scene compared to the traditional algorithm. At the same time, these experimental results also show that the algorithm can be used to obtain a short and smooth path, avoid obstacles in real time, and effectively avoid the problem of falling into a locally optimal solution.
Hand pose estimation based on improved NSRM network

Abstract Hand pose estimation is the basis of dynamic gesture recognition. In vision-based hand pose estimation, the performance of hand pose estimation is affected due to the high flexibility of hand joints, local similarity and severe occlusion among hand joints. In this paper, the structural relations between hand joints are established, and the improved nonparametric structure regularization machine (NSRM) is used to achieve more accurate estimation of hand pose. Based on the NSRM network, the backbone network is replaced by the new high-resolution net proposed in this paper to improve the network performance, and then the number of parameters is decreased by reducing the input and output channels of some convolutional layers. The experiment of hand pose estimation is carried out by using public dataset, the experimental results show that the improved NSRM network has higher accuracy and faster inference speed for hand pose estimation.
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members

In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model’s training data. Existing MIA defenses conceal the presence of a target sample through strong regularization, knowledge distillation, confidence masking, or differential privacy. We propose MIAShield, a new MIA defense based on preemptive exclusion of member samples instead of masking the presence of a member. MIAShield departs from prior defenses in that it weakens the strong membership signal that stems from the presence of a target sample by preemptively excluding it at prediction time without compromising model utility. To that end, we design and evaluate a suite of preemptive exclusion oracles leveraging model confidence, exact/approximate sample signature, and learning-based exclusion of member data points. To be practical, MIAShield splits a training data into disjoint subsets and trains each subset to build an ensemble of models. The disjointedness of subsets ensures that a target sample belongs to only one subset, which isolates the sample to facilitate the preemptive exclusion goal. We evaluate MIAShield on three benchmark image classification datasets. We show that MIAShield effectively mitigates membership inference (near random guess) for a wide range of MIAs; achieves far better privacy/utility trade-off compared with state-of-the-art defenses; and remains resilient in the face of adaptive attacks.
‘You Draw It’: Implementation of Visually Fitted Trends with r2d3

How do statistical regression results compare to intuitive, visually fitted results? Fitting lines by eye through a set of points has been explored since the 20th century. Common methods of fitting trends by eye involve maneuvering a string, black thread, or ruler until the fit is suitable, then drawing the line through the set of points. In 2015, the New York Times introduced an interactive feature, called ‘You Draw It,’ where readers are asked to input their own assumptions about various metrics and compare how these assumptions relate to reality. This research is intended to implement ‘You Draw It’, adapted from the New York Times, as a way to measure the patterns we see in data. In this paper, we describe the adaptation of an old tool for graphical testing and evaluation, eye-fitting, for use in modern web-applications suitable for testing statistical graphics. We present an empirical evaluation of this testing method for linear regression, and briefly discuss an extension of this method to non-linear applications.
Fringe pattern demodulation using the fan two-dimensional continuous wavelet transform conjugate to Shannon entropy

This paper proposes an algorithm for phase demodulating fringe patterns using a two-dimensional continuous wavelet transform (2D-CWT). This algorithm exploits the isotropy property of the fan mother wavelet conjugated to Shannon entropy to perform the ridge extraction process by using only 2D-CWT arguments. The proposed algorithm's performance is shown through simulated fringe patterns corrupted by speckle noise. Also, to evaluate the accuracy of the ridge extracted from the modulus to that obtained from the arguments of 2D-CWT, the developed algorithm is compared to the maximum ridge extraction algorithm for 2D-CWT and cost function ridge extraction algorithm for 2D-CWT, which extract the ridge from the 2D-CWT modulus. Furthermore, we demonstrate the ability of the proposed algorithm to demodulate real fringe patterns derived from optical metrology for temperature measurement. The most important result of the proposed method is that it is provably optimal in estimating the 2D-CWT ridge of oriented fringes. The added strength of the algorithm is that it is simpler and has better resistance to speckle noise than previous methods because it employs scalograms of arguments, which are the origin of the definition of the ridge.
Grouping Madura Tourism Objects with Comparison of Clustering Methods

Madura has tourist attractions spread over four regencies, one of which is Bangkalan. Bangkalan has the most tourist attractions compared to other districts in Madura. There are twenty-one attractions in Bangkalan from three categories: religion, nature, and culture. And there are several indicators that influence visitor needs in determining tourist attractions, including Name of Tourism, Type of Tourism, Region, Gender, Age, Occupation, Education, and Marital Status, as well as three groups of high, medium, and low. Clustering is a data mining process that can be applied in various fields. The method used in this study is K-Means and Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) methods. The K-Means method is a data mining method by grouping non-hierarchical data. At the same time, the DBSCAN method is a grouping method based on data density. This study compares the two methods to produce optimal solutions for classifying tourist objects. From several trials that have been carried out by comparing the two methods, the resulting accuracy value using a dataset of 21 attractions with ten criteria shows the accuracy value obtained by K-Means is superior to DBSCAN. The K-Means method has a higher Silhouette Index (SI) value of 0.6902 and k= 8.
Multi-stream GCN for Sign Language Recognition Based on Asymmetric Convolution Channel Attention

Sign Language Recognition (SLR) has become increasingly important all over the world. In this paper, we propose the Asymmetric Convolution Channel Attention module (ACCA), which uses multi-scale vertical and horizontal skeletons of convolution kernel to extract the channel characteristics. We argue that the ACCA avoids some redundant features and extracts more distinctive features. It also enhances the robustness of vertical and horizontal flipping. The backbone of our network is the SL-GCN and the dataset is the WLASL2000. As a result, the top1 accuracy of joint, bone, joint-motion, and bone-motion streams are 48.12%, 47.16%, 31.10%, and 30.09% respectively. After multi-stream fusion, we achieve 53.52%. We also put the ACCA into other models and prove the generalization. Furthermore, we achieve 54.12% based on MS-G3D, which exceeds the most advanced accuracy of the WLASL2000 dataset using the single-modal skeleton-based model.