Reviewerly/exHarmony huggingface dataset:
Human recognition with the optoelectronic reservoir-computing-based micro-Doppler radar signal processing

Current perception and monitoring systems, such as human recognition, are affected by several environmental factors, such as limited light intensity, weather changes, occlusion of targets, and public privacy. Human recognition using radar signals is a promising direction to overcome these defects; however, the low signal-to-noise ratio of radar signals still makes this task challenging. Therefore, it is necessary to use suitable tools that can efficiently deal with radar signals to identify targets. Reservoir computing (RC) is an efficient machine learning scheme that is easy to train and demonstrates excellent performance in processing complex time-series signals. The RC hardware implementation structure based on nonlinear nodes and delay feedback loops endows it with the potential for real-time fast signal processing. In this paper, we numerically study the performance of the optoelectronic RC composed of optical and electrical components in the task of human recognition with noisy micro-Doppler radar signals. A single-loop optoelectronic RC is employed to verify the application of RC in this field, and a parallel dual-loop optoelectronic RC scheme with a dual-polarization Mach-Zehnder modulator (DPol-MZM) is also used for performance comparison. The result is verified to be comparable with other machine learning tools, which demonstrates the ability of the optoelectronic RC in capturing gait information and dealing with noisy radar signals; it also indicates that optoelectronic RC is a powerful tool in the field of human target recognition based on micro-Doppler radar signals.
An Improved DWA Path Planning Algorithm Integrating Global JPS Strategy

In complex and dynamic environment, current dynamic window approach (DWA) and its improved algorithm are apt to be trapped in local minimum. It is difficult for them to obtain planning path with global optimum. They are also liable to be trapped in a second local minimum. Aiming at these problems, this paper first introduces heuristic functions by building a function of safety level and a function of cardinal direction bias; at the same time, it integrates jump point search (JPS) strategy to obtain path nodes with global optimum, which improves the efficiency of path nodes obtaining and guarantees the safety of the obtained path nodes as well. Then, the obtained path nodes with global optimum are taken as dynamically refreshed goals for DWA. Judging criteria of the second local minimum and replanning framework are set to guarantee the completeness of path planning and the global optimum of the planned path. In the end, simulations of the proposed algorithm are carried out in complex and dynamic environment. The results show that the proposed algorithm effectively solves the problems of local minimum and the second local minimum caused by dynamic obstacles; meanwhile, it also improves the efficiency of path planning and the capability to seek an optimal path.
A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System

Currently, matrix decomposition is one of the most popular collaborative filtering algorithms by using factor decomposition to effectively deal with large-scale rating matrix. Matrix decomposition mainly uses the interaction records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a new UISVD++ model that fuses the type attributes of movies and the age attributes of users into SVD++ framework. By projecting the age attribute into the user's implicit space and the type attribute into the item's implicit space, the model enriches the side information of users and items. At last, comparative experiments are done on two public datasets, Movielens-100K and Movielens-1M. Results express that the prediction accuracy of this model is better than other baselines in the task of predicting scores. In addition, the results also show that UISVD++ can effectively alleviate the cold start situation.
Collaborative Machine Learning: Schemes, Robustness, and Privacy

Distributed machine learning (ML) was originally introduced to solve a complex ML problem in a parallel way for more efficient usage of computation resources. In recent years, such learning has been extended to satisfy other objectives, namely, performing learning in situ on the training data at multiple locations and keeping the training datasets private while still allowing sharing of the model. However, these objectives have led to considerable research on the vulnerabilities of distributed learning both in terms of privacy concerns of the training data and the robustness of the learned overall model due to bad or maliciously crafted training data. This article provides a comprehensive survey of various privacy, security, and robustness issues in distributed ML.
RuBERT Embeddings in the Task of Classifying User Posts on a Social Media

This paper presents models for solving the problem of multiclass classification of user posts in a social media. These models are based on embeddings extracted from messages using the RuBert language model and a fully connected neural network built over it. The models presented are compared to a baseline model using long-term short-term memory neurons (LSTM). The results will improve the accuracy of the classification posts, which in turn will improve the accuracy of assessing the psychological characteristics users.
Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-Fluorodeoxyglucose Positron Emission Tomography

In animal experiments, ischemic stroke is usually induced through middle cerebral artery occlusion (MCAO), and quality assessment of this procedure is crucial. However, an accurate assessment method based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is still lacking. The difficulty lies in the inconsistent preprocessing pipeline, biased intensity normalization, or unclear spatiotemporal uptake of FDG. Here, we propose an image feature-based protocol to assess the quality of the procedure using a 3D scale-invariant feature transform and support vector machine. This feature-based protocol provides a convenient, accurate, and reliable tool to assess the quality of the MCAO procedure in FDG PET studies. Compared with existing approaches, the proposed protocol is fully quantitative, objective, automatic, and bypasses the intensity normalization step. An online interface was constructed to check images and obtain assessment results.
Application of Text Analysis Methods to Recommend Student Choices

The paper considers the use of natural language processing methods to recommend student choices. Descriptions of subjects taken by the student serve as the basis for compiling the student profile. This profile is used to rate available side projects as most corresponding to that student’s educational history. The quality of recommendations is evaluated based on the data from the "Individual Educational Trajectories" system.
The Applications of Blockchain in the Covert Communication

Covert communication is designed for hiding the subliminal communication which takes place between both of the speakers and their relationship. The traditional covert communication utilizes the centralized channel and the third-party central node or authority to distribute messages which leads to a lack of undetectability, antitraceability, and robustness. In recent years, there have been attempts to apply the blockchain to covert communication solutions, for the characteristics of blockchain such as decentralization, openness, and trustworthiness. In this paper, based on the analysis of the literature and the classification according to the hiding position, we identify four kinds of covert communication: Address Channel, Value Channel, DSA (Digital Signature Algorithm) Channel, and Script Channel, which will help inform future research agenda.
A Comparative Stud of Predictive Models for Pharmaceutical Sales Data

To provide their patients with the care they need as quickly as possible, pharmacies are supplied by wholesaler-distributors who provide them with a half-day delivery guarantee for most product references. For this purpose, they have set up an efficient and complex supply chain. To further improve the efficiency of their delivery services, some of them want to use machine learning tools to predict future orders and anticipate their inventory needs. This paper investigates different machine learning models for the prediction of sales on molecules of a French wholesaler-distributor. This paper focuses on four molecules and compares the results of the models predictions on these molecules.
DDPG-Based Improved Seeker Optimization Algorithm for Robot Path Planning

Robot path planning is one of the core technologies for mobile robot applications. In path planning, the traditional seeker optimization algorithm (SOA) suffers from insufficient intelligence, slow convergence, and poor solving ability in robot path planning. Therefore, this paper proposes a deep deterministic policy gradient (DDPG)-based improved seeker optimization algorithm (DDPGSOA). The DDPGSOA is successfully used in the robot path planning problem. First, this paper introduces robot map modeling and path modeling. Then, the shortcomings of the traditional SOA are analyzed and improved using the DDPG algorithm to produce the DDPGSOA algorithm. Finally, the path planning comparison experiments were conducted in MATLAB with a genetic algorithm, particle swarm optimization algorithm, gray wolf algorithm, and original seeker optimization algorithm. The experimental results show that DDPGSOA has more advantages in convergence performance and robot path solving ability.
Distributed Data Parallel Training Based on Cumulative Gradient

With the development of science and technology, the scale of deep learning models is getting larger and larger. Target detection models trained with a large amount of labeled data can achieve better performance than machine learning models, but large-scale model training is generally slower and requires high Performance workstation support. Based on the Yolov4 target detection model, this paper studies how to quickly shorten the training time of the model through distributed data parallelism. On this basis, a distributed data parallel training method with cumulative gradients is proposed. This method can solve the problem that the input cannot be increased due to the limitation of video memory. For batch problems, experiments prove that this method has a higher speedup and parallel efficiency.
Le-SKT: Lightweight traffic density estimation method based on structured knowledge transfer

The traffic density estimation algorithm usually runs on an embedded platform, and its efficiency is very important to the implementation of applications. However, most of the existing methods rely on heavy backbone networks, require high-performance hardware platform support and take a long time to calculate, which are difficult prerequisites to apply in practice. In consideration of the aforementioned problems, this paper proposes a lightweight traffic density estimation method based on structured knowledge transfer (Le-SKT). This method consists of a teacher network and student network. In the teacher network, in order to solve the problem of variable vehicle scales in video images, a multiscale fusion structure is designed, which can fully fuse the low-level and high-level features of an image. To solve the problem of background interference in video images, a fusion module based on an attention mechanism is constructed to enhance the useful traffic flow feature information and suppress the background information. At the same time, the fused traffic flow feature information is dilated convoluted to expand the receptive field and obtain richer contextual vehicle feature information, which generates a trained teacher network. Finally, a lightweight student network is generated by using the structured knowledge of the teacher network, which solves the problems of long computing times and high hardware requirements. The MAE and GAME of this method are reduced to 3.92 and 5.69, respectively in the TRANCOS dataset and 3.74 and 5.57, respectively in the VisDrone2019 vehicle dataset, respectively; The parameter quantity of the student network is only 0.65% of that of the teacher network, which greatly reduces the scale of the neural network model and is suitable for an embedded computing platform with low performance.
State transfer on paths with weighted loops

We consider the fidelity of state transfer on an unweighted path on $$n \ge 3$$ vertices, where a loop of weight w has been appended at each of the end vertices. It is known that if w is transcendental, then there is pretty good state transfer from one end vertex to the other; we prove a companion result to that fact, namely that there is a dense subset of $$[1,\infty )$$ such that if w is in that subset, pretty good state transfer between end vertices is impossible. Under mild hypotheses on w and t, we derive upper and lower bounds on the fidelity of state transfer between end vertices at readout time t. Using those bounds, we localise the readout times for which that fidelity is close to 1. We also provide expressions for, and bounds on, the sensitivity of the fidelity of state transfer between end vertices, where the sensitivity is with respect to either the readout time or the weight w. Throughout, the results rely on detailed knowledge of the eigenvalues and eigenvectors of the associated adjacency matrix.
Applicant's Decision Support System for Choosing the Direction of Study

The paper deals with the urgent task of developing a system that can improve the process of choosing an educational program by an applicant, based on his personal characteristics, interests and skills, and at the same time presented in a familiar and convenient environment for applicants. This requires establishing a correspondence between educational programs and types of socio-professional orientation, which in turn can be formalized as fuzzy variables. The purpose of the paper is to develop a tool for collecting data on students who are already university students, as well as laying the functionality for the subsequent use of this system as a recommendation for applicants.
Bit commitment as an introduction to quantum cryptography

Abstract This paper analyzes some of the drawbacks of Bennett &amp; Brassard’s quantum key distribution protocol when used in an educational setting introducing components of quantum cryptography. We propose to utilize a quantum bit commitment protocol instead in order to avoid the outlined difficulties. Additionally, we present implementations of the latter protocol in two different programming paradigms which may either be used as a practical demonstration tool for holding a quantum bit ballot, or as a means to foster an in-depth understanding of the cryptographic protocol amongst pupils with a background in computer science.
An integrated cuckoo search-genetic algorithm for mining frequent itemsets

Mining frequent itemsets is a contemporary research topic in the data-mining domain. Identifying frequent itemsets has acquired its significance due to recognized objectives in the field of association mining. The frequent itemset mining problem focuses on identifying frequent patterns and correspondence between the itemsets in the transactional database. Frequent itemset mining problems are time consuming when dealing with huge amounts of data and extracting the maximum number of the frequent itemsets from a large search space is a challenging process. Since the transactional database size is enormous, a stochastic method is applied to find optimal solutions. In this article, an integrated approach to extract frequent itemsets is proposed based on the cuckoo search and genetic algorithm. This integrated methodology is performed by merging the reproduction process of cuckoo species with the operators such as crossover and mutation in GA. First, a strategy to diversify the population and to discover maximum frequent patterns in the search process, Second, a neighborhood exploration strategy is applied for recurrent solutions to improve the accuracy. Finally, the proposed integrated approach is validated with respect to the solution quality and convergence rate. The integrated approach is verified using standard data instances. The ICSGA approach efficiently discovers the frequent patterns. The comparison results of the integrated approach are eminently productive than the other approaches in the literature.
Improving Pretrained Language Model Fine-Tuning With Noise Stability Regularization

The advent of large-scale pretrained language models (PLMs) has contributed greatly to the progress in natural language processing (NLP). Despite its recent success and wide adoption, fine-tuning a PLM often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named layerwise noise stability regularization (LNSR). Specifically, our method perturbs the input of neural networks with the standard Gaussian or in-manifold noise in the representation space and regularizes each layer's output of the language model. We provide theoretical and experimental analyses to prove the effectiveness of our method. The empirical results show that our proposed method outperforms several state-of-the-art algorithms, such as [Formula: see text] norm and start point (L2-SP), Mixout, FreeLB, and smoothness inducing adversarial regularization and Bregman proximal point optimization (SMART). In addition to evaluating the proposed method on relatively simple text classification tasks, similar to the prior works, we further evaluate the effectiveness of our method on more challenging question-answering (QA) tasks. These tasks present a higher level of difficulty, and they provide a larger amount of training examples for tuning a well-generalized model. Furthermore, the empirical results indicate that our proposed method can improve the ability of language models to domain generalization.
Improved Dual-image Quality with Reversible Data Hiding Using Translocation and Switching Strategy

Dual-image reversible data hiding (RDH) is a technique for hiding important messages.This technology can be used to safely deliver secret messages to the recipient through dual images in an open network without being easily noticed.The recipient of the image must receive the two stego-images before the secret message can be completely retrieved.Imperceptibility is one of the main advantages of data hiding technology; to increase the imperceptibility, the quality requirements of the stego-images are relatively important.A dual steganographic image RDH method, called a DS-CF scheme that can achieve a better steganographic image quality using the center folding (CF) strategy.In this paper, we developed a translocation and switching strategy (TaS) to shorten the distances between the stego-pixel coordinates and the cover pixel coordinates after information being hidden.Compared with the DS-CF scheme, our proposed DS-TaS scheme can effectively improve the quality of the steganographic images at the same level of embedding capability.The experimental results show that the PSNR of our DS-TaS scheme at k = 1 was 55.66 dB, which is an increase of 1.5 dB, and is 51.43 dB for k = 2, 46.66 dB for k = 3, and 40.91 dB for k = 4.In addition, the PSNR values of the stego images was increased by 1.5, 0.29, 0.29, and 0.19 dB, respectively.This shows that our proposed dual-image RDH method can optimize the visual quality of the stego-images and is better than many other dual-image RDH techniques.
Secure Transaction Approach in Health Care Using BlockChain

In the medical field, block chain is being used to maintain the share and access of patient data. Openness and security standards for the interoperability system will be enhanced. The study has reviewed the technical implementations of blockchain and how the distributed technology has supported Patient-Centric health care records in Medicare billing and prescription data. In terms of healthcare, blockchain technology's benefits and characteristics are discussed. The traceability of healthcare information is improved with the help of blockchain technology.
Robust Sentimental Class Prediction Based on Cryptocurrency-Related Tweets Using Tetrad of Feature Selection Techniques in Combination with Filtered Classifier

Individual mental feelings and reactions are getting more significant as they help researchers, domain experts, businesses, companies, and other individuals understand the overall response of every individual in specific situations or circumstances. Every pure and compound sentiment can be classified using a dataset, which can be in the form of Twitter text by various Twitter users. Twitter is one of the vital platforms for individuals to participate and share their ideas about different topics; it is also considered to be one of the most famous and the biggest website for micro-blogging on the Internet. One of the key purposes of this study is to classify pure and compound sentiments based on text related to cryptocurrencies, an innovative way of trading and flourishing daily. The cryptocurrency market incurs many fluctuations in the coins’ value. A small positive or negative piece of news can sensate the whole scenario about the specific cryptocurrencies. In this paper, individuals’ pure and compound sentiments based on cryptocurrency-related Twitter text are classified. The dataset is collected through the Twitter API. In WEKA, the two deployment schemes are compared; firstly, straight with single feature selection technique (Tweet to lexicon feature vector), and secondly, a tetrad of feature selection techniques (Tweet to lexicon feature vector, Tweet to input lexicon feature vector, Tweet to SentiStrength feature vector, and Tweet to embedding feature vector) are used to purify the data LibLINEAR (LL) classifier, which contains fast algorithms for linear classification using L2-regularization L2-loss support vector machines (Dual SVM). The LL classifier differs in that it can potentially alleviate the sum of the absolute values of errors rather than the sum of the squared errors and is typically much speedier. Based on the overall performance parameters, the deployment scheme containing the tetrad of feature selection techniques with the LL classifier is considered the best choice for the purpose of classification. Among machine learning techniques, LL produces effective results and gives an efficient performance compared to other prevailing techniques. The findings of this research would be beneficial for Twitter users as well as cryptocurrency traders.
PROTOCOL: Hate online and in traditional media: A systematic review of the evidence for associations or impacts on individuals, audiences, and communities

Abstract This is the protocol for a Campbell systematic review: The objectives are as follows: (1) to critically and systematically synthesize the empirical evidence on the effects or impacts of exposure to or consumption, active search, or promotion of hate content online or in traditional media; (2) to describe how the characteristics of hate (e.g., type of content, ideologies, severity, type of platform) impact the documented effects; (3) to collect and identify the role of contextual variables (e.g., individual traits, age, gender, socio‐economic background) on the documented effects; (4) to collect and produce a meaningful classification of outcomes; and (5) to identify gaps and limitations in the research and related policy documents.