Reviewerly/exHarmony huggingface dataset:
Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.
Parallel genetic algorithm for Travelling Salesman Problem

Travelling Salesman Problem (TSP) returns the minimal length of traveling distance to travel through all the cities given by the data set and returning to the starting position, which allows the users to optimize their travel path to minimize total cost. Genetic algorithm (GA) is used to solve the problem due to its efficiency. However, as the sample size increases, the runtime of GA for TSP increases significantly. This paper introduces three new parallel GA (PGA) – Master-Slave model, Coarse-Grained model, and Combined PGA (MSCG)–to solve TSP to optimize performance and minimize runtime. The first two performs parallelization on a different level and Combined PGA is the combination of Master-Slave and Coarse- Grained models. The Master-slave model paralyzes the evolution process and divides the population into threads to enter the calculation. Coarse-grained separates the population even earlier, so before entering evolution, the populations are already divided into subsets. MSCG separates populations both before and after entering evolution to get the advantages of both approaches. To verify the effectiveness of the three proposed methods, we compare them with the non-parallelized GA. The results show that the master-slave method generally would produce on average 10% shorter route than the coarsegrained method but would have on average 40% higher time usage.
Synchronous monitoring of axial vibration and rotation speed of rotating cylinder by linear array scanning

The axial vibration (AV) and rotation speed (RS) signals of a rotating cylinder contain rich condition information. In order to harness such quantitative information, an effective measurement method for synchronous monitoring of the AV and RS is highly desirable. However, traditional approaches are restricted either by limited test distance or multiple isolated sensors, which makes the approaches impractical to synchronously acquire the AV and RS signals through a single device. In this paper, a generalized visual measurement method for synchronous monitoring of the AV and RS via linear array scanning (LAS) is proposed. The basic idea is to paste a mark containing a printed black continuous bar and a black block (BCBB) on the surface of a rotating cylinder, and use a linear array camera to synchronously sample the bar and blocks. For the extraction of mark edges, we propose a single edge pixel extraction method through gray gradient of row pixels based on the observation of relatively stable gray level distribution. Line scanned signals disturbed by seam error or axial offset are automatically corrected through seam compensation and axial offset correction respectively. Experimental validation of the proposed method is presented for the cylinder under static hammering, variable speed condition and steady speed condition at a scanning distance of 500 mm. Bilinear interpolation is applied to the scanned linear array images before extracting the edge features and helps improve measurement accuracy. Analyses in the time domain and frequency domain verify the effectiveness of the LAS. The average error between the RS signal obtained by LAS and the reference value is 0.1420 rpm. The correlation coefficient between the AV time-domain signal of LAS and the reference signal is up to 0.900 and the resonance frequency error in the frequency spectrum is less than 1 Hz.
A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.
Mighty Belief Revision

Abstract Belief revision theories standardly endorse a principle of intensionality to the effect that ideal doxastic agents do not discriminate between pieces of information that are equivalent within classical logic. I argue that this principle should be rejected. Its failure, on my view, does not require failures of logical omniscience on the part of the agent, but results from a view of the update as mighty : as encoding what the agent learns might be the case, as well as what must be. The view is motivated by consideration of a puzzle case, obtained by transposing into the context of belief revision a kind of scenario that Kit Fine has used to argue against intensionalism about counterfactuals. Employing the framework of truthmaker semantics, I go on to develop a novel account of belief revision, based on a conception of the update as mighty, which validates natural hyperintensional counterparts of the usual AGM postulates.
Cyber Security Trend Analysis

The last few years are known as the era of digitization, which also focuses on Industry 4.0. On one side, these latest tools and techniques like IoT, big data analytics, blockchain help the organizations to make better decision making and better future predictions in terms of processes and services; on the other hand, it also leads to the new problem known as cybercrime. Keeping data on the cloud and processing it using cloud-based technology gives more opportunities to hackers to misuse things. Many governments and organizations invest in huge amounts to train their employees and save their data from such cybercrimes. These cybercrimes not only impact organizations but also the females and children of the countries. The purpose of this study is to know the current state of cybercrime in India and to identify the impact of cybercrime on females and children.
Awareness Regarding the Implication of Artificial Intelligence in Science Education among Pre-Service Science Teachers

This study aimed to identify pre-service science teachers' awareness of employing artificial intelligence in science education.It sought also to determine the reasons that led to this level of pre-service science teachers' awareness of employing AI in science education.The mixed-method was used with an interpretive sequential design.The researcher prepared a measurement and distributed it to all forty-three pre-service science teachers.In terms of the qualitative method, interviews were conducted with a sample consisting of 15 female pre-service teachers.The quantitative study revealed pre-service science teachers' awareness of employing AI in science education low level.The results of the qualitative interviews used to explain the results of the quantitative study.There are statistically significant differences, at a level of confidence of 0.01, between the hypothetical and actual means of the total score of pre-service science teachers' awareness of employing AI in science education, as well as in the case of dimensions.The differences are in favor of the hypothetical mean.
Comparative Analysis of Dynamic Characteristics between Electronic Signature and Conventional Signature Based on Computer Vision Technology

During the last two or three decades where innovations in technology have been dominant, especially the rapid development of electronic information technology, various types of electronic devices have been developed for different application areas. It is this technological-assisted equipment that has drastic effects on the lifestyle of every creature in general and human beings in particular. In addition to the other activities or services, technology has enabled human beings to write on electronic devices, which is due to the fact that these devices will generate electronic signature handwriting that is extremely useful for human beings. They may effectively cope with the electronization of traditional signature handwriting and ease the difficulties of authenticating the identification information of the signatory of electronic documents when used in conjunction with electronic documents. This method is frequently utilized in e-government, e-commerce, banking and insurance, medical care, and other industries. This study uses the current mainstream computer vision technology to compare and analyze the handwriting dynamic characteristics of electronic signature and conventional signature. It uses the electronic signature God and software to collect and extract the original characteristic data of user's electronic signature and then extracts the characteristics of average writing speed, duration, and average pressure on the basis of these data for analysis. Among these techniques, the writing time of electronic signature is longer than that of conventional signature, and the average speed of conventional signature notes is higher than that of electronic signature, and when analyzing the average pressure characteristics, the conventional signature pressure is greater than the electronic signature pressure.
A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study

The ever-increasing volume of medical literature necessitates the classification of medical literature. Medical relation extraction is a typical method of classifying a large volume of medical literature. With the development of arithmetic power, medical relation extraction models have evolved from rule-based models to neural network models. The single neural network model discards the shallow syntactic information while discarding the traditional rules. Therefore, we propose a syntactic information-based classification model that complements and equalizes syntactic information to enhance the model.We aim to complete a syntactic information-based relation extraction model for more efficient medical literature classification.We devised 2 methods for enhancing syntactic information in the model. First, we introduced shallow syntactic information into the convolutional neural network to enhance nonlocal syntactic interactions. Second, we devise a cross-domain pruning method to equalize local and nonlocal syntactic interactions.We experimented with 3 data sets related to the classification of medical literature. The F1 values were 65.5% and 91.5% on the BioCreative ViCPR (CPR) and Phenotype-Gene Relationship data sets, respectively, and the accuracy was 88.7% on the PubMed data set. Our model outperforms the current state-of-the-art baseline model in the experiments.Our model based on syntactic information effectively enhances medical relation extraction. Furthermore, the results of the experiments show that shallow syntactic information helps obtain nonlocal interaction in sentences and effectively reinforces syntactic features. It also provides new ideas for future research directions.
S-RAP: relevance-aware QoS prediction in web-services and user contexts

With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical techniques on historical data of user experiences of the available items/products. Research efforts provide several methods for web-service recommendation in which QoS-related attributes play primary role such as response-time, throughput, security, privacy and web-service-delivery. Derivable attributes including, user-trustworthiness and web-services reputation in contexts of users and web-services can also affect the QoS prediction. The proposed research focuses on a web-service recommendation model, S-RAP, for QoS prediction based on derivable attributes to predict QoS of a web-service that a user who has not invoked it before would experience. Services-Relevance attribute is proposed in this publication, which emphasizes on employing the historical data and extracting the degree of relevance in the users and web-services context to predict the QoS values for a user. The proposed system produces satisfactorily accurate rating predictions in the experiments evaluated by the Mean Absolute Error and Normalized Mean Absolute Error metrics. The results compared with state-of-the-art models show a relative improvement by 4.0%.
Incremental Feature Spaces Learning with Label Scarcity

Recently, learning and mining from data streams with incremental feature spaces have attracted extensive attention, where data may dynamically expand over time in both volume and feature dimensions. Existing approaches usually assume that the incoming instances can always receive true labels. However, in many real-world applications, e.g., environment monitoring, acquiring the true labels is costly due to the need of human effort in annotating the data. To tackle this problem, we propose a novel incremental Feature spaces Learning with Label Scarcity (FLLS) algorithm, together with its two variants. When data streams arrive with augmented features, we first leverage the margin-based online active learning to select valuable instances to be labeled and thus build superior predictive models with minimal supervision. After receiving the labels, we combine the online passive-aggressive update rule and margin-maximum principle to jointly update the dynamic classifier in the shared and augmented feature space. Finally, we use the projected truncation technique to build a sparse but efficient model. We theoretically analyze the error bounds of FLLS and its two variants. Also, we conduct experiments on synthetic data and real-world applications to further validate the effectiveness of our proposed algorithms.
Revisiting the Polynomial-Time Equivalence of Computing the CRT-RSA Secret Key and Factoring

The Rivest–Shamir–Adleman (RSA) cryptosystem is currently the most influential and commonly used algorithm in public-key cryptography. Whether the security of RSA is equivalent to the intractability of the integer factorization problem is an interesting issue in mathematics and cryptography. Coron and May solved the above most fundamental problem and proved the polynomial-time equivalence of computing the RSA secret key and factoring. They demonstrated that the RSA modulus N=pq can be factored in polynomial time when given RSA key information (N,e,d). The CRT-RSA variant is a fast technical implementation of RSA using the Chinese Remainder Theorem (CRT), which aims to speed up the decryption process. We focus on the polynomial-time equivalence of computing the CRT-RSA secret key and factoring in this paper. With the help of the latest partial key exposure attack on CRT-RSA, we demonstrate that there exists a polynomial-time algorithm outputting the factorization of N=pq for edp,edq&lt;N3/2 when given the CRT-RSA key information (N,e,dp,dq). We apply Coppersmith’s lattice-based method as a basic mathematical tool for finding the small root solutions of modular polynomial equations. Furthermore, we provide validation experiments to illustrate the correctness of the CRT-RSA modulus factorization algorithm, and show that computing the CRT-RSA secret key and factoring its modulus is polynomial-time equivalent by using concrete numerical examples.
The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

Purpose This paper purposed a multi-facet sentiment analysis system. Design/methodology/approach Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters. Findings The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems. Originality/value The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.
Widespread Virtual Life with Blockchain, Deepfake, Avatar, Cryptocurrency, NFT and Metaverse

The idea of living in different worlds, starting with cinema, can be seen as the beginning of the virtual world. Because, although limited for a certain period of time, cinema and television, which disconnected the audience from the real world, allowed them to get away from real life and imagine different lives. Later, with the invention of the internet, the power of the computer increased and virtual life continued to be offered to users through websites and games. With the nickname feature, users are able to take their place in the v,rtual world with different identities other than their own has also started. This understanding formed the basis of the understanding of avatars. Especially with computer games, the use of avatars has increased as each user starts to create their own avatar in the virtual world. Big data was created by archiving personal data with social media, and cryptocurrencies and deepfake applications developed by making virtual life more professional with virtual reality/augmented reality were offered to users. In the research, the development of this whole process and the positive and negative effects of Metaverse, which companies such as Facebook, Microsoft, Epic, Roblox, Nvidia, Unity Software, continue to design rapidly, on people and real life are emphasized.
An online algorithm for optimally releasing multiple on-demand instances in IaaS clouds

With the popularity of cloud computing increasing rapidly in recent years, the use of cloud, represented by IaaS and SaaS, is being embraced by more and more users. One of the flexibility of the cloud lies in its pay-as-you-go usage model, which allows users to purchase and release cloud instances on their own demand, reducing the possible financial loss caused by wasted resources. For a SaaS provider that uses the pay-as-you-go payment model to purchase cloud instances, it is important to make a reasonable decision on when to release as many as idle on-demand cloud instances to achieve cost savings when the number of incoming user demands is in a declining phase, taking into account the cost of the start-up time to acquire new cloud instances and the penalty cost that may be incurred while SaaS users wait. In order to make optimal decisions when there is not enough knowledge to predict the future trend of incoming demands, we propose an online instance releasing algorithm which can effectively help SaaS providers to reduce the cost when using on-demand instances. Through theoretical analysis we show our online algorithm can achieve a competitive ratio of less than 2 for a variety of penalty functions. Our extensive simulation experiments based on both real Google workload data and simulated demand sequences demonstrate that the proposed online algorithm is stable and efficient.
Non-classical properties of superposition thermal quantum states

We study the statistical properties of non-classical states introduced by the superposition of a coherent with a number (Fock) state, in contact with a heat-bath. Two situations are investigated: one starts with the Gibbs distribution, such that photons are added in; the other, photons are subtracted out. Considering the use of such states in protocols for, as an example, teleportation, we identify the range of temperature, where the quantum nature of such states is stable. Finally, we propose a set of non orthonormal Bell states by adding discrete variables (Fock states) to the definition of the Bell-Cat states, which in the original definition carry only continuous variables (coherent states). We leave as perspectives the study of the viability of the use of this new set of states as building blocks of technologies which explore entanglement and teleportation protocols such as quantum repeater and quantum internet.
Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis

Diagnostic tests play an important role in medical research and clinical practice. The ultimate goal of a diagnostic test is to distinguish between diseased and nondiseased individuals and before a test is routinely used in practice, it is a pivotal requirement that its ability to discriminate between these two states is thoroughly assessed. The overlap coefficient, which is defined as the proportion of overlap area between two probability density functions, has gained popularity as a summary measure of diagnostic accuracy. We propose two Bayesian nonparametric estimators, based on Dirichlet process mixtures, for estimating the overlap coefficient. We further introduce the covariate-specific overlap coefficient and develop a Bayesian nonparametric approach based on Dirichlet process mixtures of additive normal models for estimating it. A simulation study is conducted to assess the empirical performance of our proposed estimators. Two illustrations are provided: one concerned with the search for biomarkers of ovarian cancer and another one aimed to assess the age-specific accuracy of glucose as a biomarker of diabetes.