Reviewerly/exHarmony huggingface dataset:
AI-Based Workout Assistant and Fitness Guide

Health and Fitness play a vital role in our day-today life. This can be attained in many ways of which exercising is one. Performing exercise can help us maintain very good health, only if carried out properly and in a defined manner, else the repercussions may have adverse effects on our body. To tackle this issue, we have created a system that keeps track of body movements and provides us with the number of repetitions performed, if performed within the foundation of the model. Our system also provides audio instruction to the user when performing the exercise inappropriately, and with the assistance of the user's physical measurements and his/her diet, the system is able to keep track of the user's calorie intake and recommends a certain amount of calorie intake to be followed in order to achieve normal Body Mass Index in order to stay fit. The proposed system uses Mediapipe Pose Estimation Model to track body moments while performing the exercise.
Syntactic complexity in legal translated texts and the use of plain English: a corpus-based study

Abstract This study compared the linguistic difficulty of legal translated texts with the syntactic complexity of native English legal writings in order to demonstrate the statistically significant differences between the two big datasets. The study applies features of the syntactic complexity of sentences within legal text translations that translated texts are less complicated than their original counterparts. It provides an example of how easy plain English translation in legal communication might result in understandable target writings. The findings of the legal translation of the people of three regions reveal striking patterns in terms of syntactic complexity and legal communication in plain English, which are consistent with previous research. Complex nominal and hypotactic structures result in a high number of propositions per sentence, placing a high demand on the cognitive processing abilities of those who read and understand the text. The statistics show considerable differences among the three locations and various forms of company law corpora. The study is the first large-scale quantitative analysis of the accessibility of legal jargon compared to other forms of English, emphasizing the efficacy of plain-language initiatives in legal translations.
Fine Classification Method for Massive Microseismic Signals Based on Short-Time Fourier Transform and Deep Learning

Numerous microseismic signals are produced by rock mass fracture during earthquakes, geological disasters, or underground excavations. Moreover, a large amount of noise signals are captured during microseismic signal monitoring. Specifically, some noise signals closely resemble microseismic signals, which severely impedes the rapid and accurate detection of the latter and the assessment of geological hazards. Therefore, we propose a precise model for identifying and classifying microseismic signals based on deep learning technology and short-time Fourier transform (STFT) technology. First, the STFT time–frequency analysis reveals the unique characteristics of noise, microseismic, and blasting signals, thereby allowing noise signals that are very similar to microseismic signals in the time domain to be finely distinguished. Second, the introduced attention mechanism focuses the classification on essential signal features. Finally, because tens of thousands of actual monitoring data points are considered, the deep neural network for microseismic classification is trained and tested under complex geological engineering conditions. The results demonstrate that the neural network model has good time–frequency feature extraction ability, and the well-trained model can satisfactorily complete daily classifications. Moreover, the model performs well when classifying similar noise and low-SNR microseismic signals. We believe that this type of signal-processing method, which considers multiple perspectives, can be extended to data processing in many other data-driven fields.
A hybrid recommender system using topic modeling and prefixspan algorithm in social media

Abstract Route schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users’ attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts’ similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns’ mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation.
Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction

The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This investigation aims to decrease the additional cost by introducing probabilistic multi-task intra-hour solar forecasting (feasible in real time applications) to increase the penetration of photovoltaic systems in power grids. The direction of moving clouds is estimated in consecutive sequences of sky images by extracting features of cloud dynamics with the objective of forecasting the global solar irradiance that reaches photovoltaic systems. The sky images are acquired using a low-cost infrared sky imager mounted on a solar tracker. The solar forecasting algorithm is based on kernel learning methods, and uses the clear sky index as predictor and features extracted from clouds as feature vectors. The proposed solar forecasting algorithm achieved 16.45\% forecasting skill 8 minutes ahead with a resolution of 15 seconds. In contrast, previous work reached 15.4\% forecasting skill with the resolution of 1 minute. Therefore, this solar forecasting algorithm increases the performances with respect to the state-of-the-art, providing grid operators with the capability of managing the inherent uncertainties of power grids with a high penetration of photovoltaic systems.
Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?

Recent studies show that models trained on synthetic datasets are able to outperform models trained on real-world datasets for generalizable person re-identification (GPReID). On the other hand, due to the limitations of real-world person ReID datasets, it would also be important and interesting to use large-scale synthetic datasets as test sets to benchmark algorithms. Yet this raises a critical question: is synthetic dataset reliable for benchmarking GPReID? In the literature there is no evidence showing this. To address this, we design a method called Pair-wise Ranking Analysis (PRA) to quantitatively measure the ranking similarity and perform the statistical test of identical distributions. Specifically, we employ Kendall rank correlation coefficients to evaluate pairwise similarity values between algorithm rankings on different datasets. Then, a non-parametric two-sample Kolmogorov-Smirnov (KS) test is performed for the judgement of whether algorithm ranking correlations between synthetic and real-world datasets and those only between real-world datasets lie in identical distributions. We conduct comprehensive experiments, with ten representative algorithms, three popular real-world person ReID datasets, and three recently released large-scale synthetic datasets. Through the designed pairwise ranking analysis and comprehensive evaluations, we conclude that a recent large-scale synthetic dataset ClonedPerson can be reliably used to benchmark GPReID, statistically the same as real-world datasets. Therefore, this study guarantees the usage of synthetic datasets for both source training set and target testing set, with completely no privacy concerns from real-world surveillance data. Besides, the study in this paper might also inspire future designs of synthetic datasets.
Global Combination and Clustering based Differential Privacy Mixed Data Publishing

With the rapid advancement of information technology, a large amount of high-value data have been generated. To exploit the potential value of big data and at the same time to protect individuals' sensitive information, a global combination and clustering based differential privacy (DP) mixed data publishing method is proposed in this paper. The main idea of the proposed method is to improve the truthfulness of the published data as well as to enhance the utility by shifting the sensitivity of query function from a single record to a group of records using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -median clustering algorithm. Specifically, to improve the accuracy and utility of categorical attributes, a global combination method is proposed to take the correlation among categorical attributes into account. The proposed combination method takes all categorical attributes as a unit and then applies the exponential mechanism to improve the data utility. Then we combine it with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -median clustering with differential privacy to publish the mixed data. Theoretical analysis shows that the proposed method satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -differential privacy. Experimental results on real datasets illustrate that the proposed method has a much lower information loss and time overhead than the state-of-the-art approach for the same parameters.
Exploring High-Order Spatio–Temporal Correlations From Skeleton for Person Re-Identification

Person re-identification (Re-ID) has become a hot research topic due to its widespread applications. Conducting person Re-ID in video sequences is a practical requirement, in which the crucial challenge is how to pursue a robust video representation based on spatial and temporal features. However, most of the previous methods only consider how to integrate part-level features in the spatio-temporal range, while how to model and generate the part-correlations is little exploited. In this paper, we propose a skeleton-based dynamic hypergraph framework, namely Skeletal Temporal Dynamic Hypergraph Neural Network (ST-DHGNN) for person Re-ID, which resorts to modeling the high-order correlations among various body parts based on a time series of skeletal information. Specifically, multi-shape and multi-scale patches are heuristically cropped from feature maps, constituting spatial representations in different frames. A joint-centered hypergraph and a bone-centered hypergraph are constructed in parallel from multiple body parts (i.e., head, trunk, and legs) with spatio-temporal multi-granularity in the entire video sequence, in which the graph vertices representing regional features and hyperedges denoting relationships. Dynamic hypergraph propagation containing the re-planning module and the hyperedge elimination module is proposed to better integrate features among vertices. Feature aggregation and attention mechanisms are also adopted to obtain a better video representation for person Re-ID. Experiments show that the proposed method performs significantly better than the state-of-the-art on three video-based person Re-ID datasets, including iLIDS-VID, PRID-2011, and MARS.
Semantic-aware unsupervised domain adaptation for height estimation from single-view aerial images

Traditional acquisition of height data to generate normalized digital surface models (nDSMs) of very high spatial resolution is time-consuming and expensive. Height estimation by means of optical remote sensing images is a more efficient and timely way to do so. Recent studies employed supervised learning methods. State-of-the-art computer vision methods, however, overlook semantic consistency during remote sensing image translation and neglect multi-task correlations for specific task learning. To address these problems, this paper proposes a semantic-aware unsupervised domain adaptation method for height estimation. The method consists of image translation and multitask representation learning for height estimation. We tested the transferability of our method from the ISPRS Postdam data set to the Vaihingen data set and a custom dataset of Enschede. In the image translation task, our method improved the Fréchet Inception Distance (FID) metric by at least 12.8% and 12.1% on the two datasets, respectively. In the height estimation task, our method achieved RMSEs of 3.257 m and 3.875 m, which are at least 0.603 m and 0.072 m lower than the compared unsupervised algorithm, and achieved competitive results with the supervised learning algorithm. Our results show the advantages of the proposed method in height estimation and image translation as compared to alternative strategies. We conclude that adding semantic supervision improves height estimation from single-view orthophoto under unsupervised domain adaptation. It also alleviates the problem of limited access to nDSM data for training the method.
Embedding color watermark image to color host image based on 2D-DCT

For protecting the copyright of color image, a blind watermarking algorithm for color digital images is proposed in this paper. First, affine transform and Arnold transform are successively used in the pre-processing stage to encrypt watermark information twice, which greatly improves security of watermarking algorithm. Then, the different embedding coefficients in the different image blocks and the different quantization steps in the different image layers are selected to modify part of the middle-frequency coefficients after two-dimensional discrete cosine transform (2D-DCT) for embedding the color digital watermark, which helps to improve the invisibility and robustness of watermark. Finally, the relationship between coefficient pairs of 2D-DCT is used to realize the blind extraction of the watermark information. By comparing the proposed algorithm to other relevant watermarking algorithms after many experiments, we can see that the proposed watermarking algorithm has better invisibility, stronger robustness, larger capacity and higher security.
Revisiting self-interference in Young’s double-slit experiments

Quantum superposition is the heart of quantum mechanics as mentioned by Dirac and Feynman. In an interferometric system, single photon self-interference has been intensively studied over the last several decades in both quantum and classical regimes. In Born rule tests, the Sorkin parameter indicates the maximum number of possible quantum superposition allowed to the input photons entering an interferometer, where multi-photon interference fringe is equivalent to that of a classical version by a laser. Here, an attenuated laser light in a quantum regime is investigated for self-interference in a Mach-Zehnder interferometer, and the results are compared with its classical version. The equivalent result supports the Born rule tests, where the classical interference originates in the superposition of individual single-photon self-interferences. This understanding sheds light on the fundamental physics of quantum features between bipartite systems.
Quantum multi-programming for Grover’s search

Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP) algorithm for Grover's search. Our algorithm decomposes Grover's algorithm by the partial diffusion operator and executes the decomposed circuits in parallel by QMP. We proved that this new algorithm increases the rotation angle of the Grover operator which, as a result, increases the success probability. The new algorithm is implemented on IBM quantum computers and compared with the canonical Grover's algorithm and other variations of Grover's algorithms. The empirical tests validate that our new algorithm outperforms other variations of Grover's algorithms as well as the canonical Grover's algorithm.
Trustworthiness models to categorize and prioritize code for security improvement

The exploitation of software security vulnerabilities can have severe consequences. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing costs and shortening the time to market. In this paper, we propose an approach that uses security evidences (e.g., software metrics, bad smells) to feed a set of trustworthiness models, which allow characterizing code from a security perspective. In practice, the goal is to identify the code units that are more prone to be vulnerable (i.e., are less trustworthy from a security perspective), thus helping developers to improve their code. A clustering-based approach is used to categorize the code units based on the combination of the scores provided by several trustworthiness models and taking into account the criticality of the code. To instantiate our proposal, we use a dataset of software metrics (e.g., CountLine, Cyclomatic Complexity, Coupling Between Objects) for files and functions of the Linux Kernel and Mozilla Firefox projects, and a set of machine learning algorithms (i.e., Random Forest, Decision Tree, SVM Linear, SVM Radial, and Xboost) to build the trustworthiness models. Results show that code that is more prone to be vulnerable can be effectively distinguished, thus demonstrating the applicability and usefulness of the proposed approach in diverse scenarios.
Predicting body fat using a novel fuzzy-weighted approach optimized by the whale optimization algorithm

Obesity, termed as excessive body fat, is a major public health problem. Being able to accurately predict body fat percentage provides an opportunity for making appropriate prevention and diagnosis. Although various assessment techniques have been applied for body fat estimation, high-cost tests are required using special equipment, and the performance of standard machine learning models are typically impacted by noise data. In this paper, we propose a novel fuzzy-weighted approach for improving body fat prediction using easily accessed body measurements in a cost-effective manner. Given the noise data problem, we design a novel fuzzy weighted operation by extending the minimum and maximum values for calculating the fuzzy weights to cover more possibilities. The fuzzy weights are incorporated into the error constraints of a relative error support vector machine model to improve its robustness to noise data. In addition, the Whale Optimization Algorithm (WOA) is applied in our proposed model for parameter optimization. Our proposed fuzzy-weighted approach delivers excellent results for body fat prediction by outperforming other well-known machine learning models. Further, the statistical test based on the 20 runs of experiments not only confirms that our proposed model could significantly outperform the models under comparison, but also show the fuzzy weighted operation and WOA are effective to improve the proposed approach. This offers a viable alternative for medical decision makers to make appropriate prevention, early diagnosis, and early treatment of body fat related diseases when high-cost measurement techniques are not available.
Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.
Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

Abstract Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships.