Reviewerly/exHarmony huggingface dataset:
Recursive Algorithms for the Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

In this article, two recursive algorithms for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived.They are based on a maximum likelihood approach and combine an innovative single-tone estimator with a serial cancellation procedure.Our numerical results lead to the conclusion that the developed methods can achieve a substantially better accuracy-complexity tradeoff than various related techniques in the presence of multiple closely spaced tones.Moreover, they can be exploited to detect multiple closely spaced targets and estimate their spatial coordinates in multiple-input multiple-output frequency-modulated continuous wave radar systems.
A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Computer-aided sports systems are an important area of current research. To be specific, it can combine computer vision, computer graphics, and motion capture technologies with the characteristics of sports, and design and develop assisted systems according to user needs. As a result, it is of great practical importance and application to improve the entertainment and science of sports learning, to stimulate public participation in sports, and to learn sports skills. Table tennis has a very great popular base in China and has a wide range of audiences. However, for beginners or amateurs of table tennis, it is difficult to learn table tennis skills by relying on books, videos, etc., due to the lack of professional technical guidance, which makes it relatively difficult for them to continuously improve their skills. Sometimes they tend to form wrong technical habits, which are not easy to detect and correct in time, and can lead to sports injuries over time. Therefore, the lack of professional instruction has hindered the development of table tennis in public fitness to a certain extent. On the other hand, in a table tennis course, the learning of table tennis techniques is mostly dependent on the teacher’s explanation. Students are bored with the repetitive and boring teaching because they need to repeat the exercises several times. The formation of correct table tennis movements requires repeated instruction from the instructor to address problems with the student’s movements. However, due to limited teaching resources, it is difficult for teachers to accommodate different students during the teaching process. In addition, with a limited team of instructors, the level of proficiency varies, making it difficult for them to make an objective assessment. As a result, with the rapid development and application of modern science and technology in the field of sports, there is an urgent need to explore intelligent sports technology instructional support systems to improve the shortcomings of the traditional teaching model. Motion recognition technology, as an essential branch of multimodal interaction technology, has been developing rapidly in recent years. This research focus on designing and implementing a system for recognizing human motion based on skeletal point pose information by combining inertial sensors. This system can collect data from sensors located at the main skeletal points of the human body and transmits them to the host computer through multi-Bluetooth pairing transmission. After that, support vector machines are applied to classify human movements and to recognize general human movements. This system has significant advantages for human movement recognition and classification due to the distinctive technical characteristics of table tennis. The recognition and classification of both players in the video can play an important role in the technical analysis and tactical arrangement of the players. As a result, the table tennis motion correction system based on human motion feature recognition has crucial research significance and application value.
Application and Analysis of Artificial Intelligence in College Students’ Career Planning and Employment and Entrepreneurship Information Recommendation

With the arrival of graduation season, the number of graduates is expanding every year, and the employment rate of college students has become one of the data that colleges and universities pay attention to. To improve the employment rate of college students, we should first educate each college student in career planning, so that they can clearly understand their own positioning and future development direction. Career planning can guide students to understand their jobs and analyze their professional fields. Faced with massive employment information, it is also a big problem for college students to search and choose information. In this paper, the personalized recommendation system for entrepreneurship is described, and the basic principles of information recommendation are described. The basic information and personal interest points of college students are represented by feature vectors, which provide a favorable theoretical support for college students’ career planning and employment and entrepreneurship information recommendation. An information recommendation model under deep learning is formed. Finally, the performance of the model under the traditional algorithm and the optimized information recommendation model is evaluated, and the satisfaction of users to this system is scored so as to provide a convenient and quick information recommendation system for college students, thus indirectly improving the employment rate of graduates and providing corresponding solutions to the problem of difficult employment.
Secure and Non-interactive k-NN Classifier Using Symmetric Fully Homomorphic Encryption

"Machine learning as a service" (MLaaS) in the cloud accelerates the adoption of machine learning techniques. Nevertheless, the externalization of data on the cloud raises a serious vulnerability issue because it requires disclosing private data to the cloud provider. This paper deals with this problem and brings a solution for the K-nearest neighbors (k-NN) algorithm with a homomorphic encryption scheme (called TFHE) by operating on end-to-end encrypted data while preserving privacy. The proposed solution addresses all stages of k-NN algorithm with fully encrypted data, including the majority vote for the class-label assignment. Unlike existing techniques, our solution does not require intermediate interactions between the server and the client when executing the classification task. Our algorithm has been assessed with quantitative variables and has demonstrated its efficiency on large and relevant real-world data sets while scaling well across different parameters on simulated data.
Optimising Photovoltaic Farm Location Using a Capabilities Matrix and GIS

Renewable energy sources provide an important solution in environmental protection activities and in the process of shaping sustainable development. The search for optimal locations enabling full exploitation of the energy intensity of real estate presents a significant challenge in terms of geoinformation analysis methods in a GIS environment. The aim of the study was to develop a capabilities matrix for the location of photovoltaic farms and, based on this, to compile a map of decision alternatives for these locations. The first stage involved the determination of the spatial features (stimulants and destimulants), which were significant in the context of photovoltaic (PV) farm location. During the analysis, the scope of the necessary data and their sources, which included topographic vector studies, aerial images, and the OpenStreetMap open data, were determined. The next stage was to determine the weights of the features which were significant in the context of photovoltaic (PV) farm location. To this end, the Multicriteria Decision Making (MCDM) method, including the Analytic Hierarchy Process (AHP) method, was employed. For the verification of the results, the entropy measure was also used. Entropy was calculated based on the diversity of previously identified geospatial features that shape the optimum conditions for their location, based on the photovoltaic farms already existing in Poland. A total of 555 photovoltaic farms were analysed. The next stage assumed the performance of geoinformation analyses using GIS tools and the development of a capabilities matrix for the PV farm location for the selected commune in Poland. The final stage involved the compilation of a PV decision alternative map for the selected commune based on the capabilities matrix. As a result, as an example, a ranking of plots was developed as decision-making alternatives for the municipality of Czarnia located in the northeastern part of Poland. It shows which parcels of land primarily have the dimension of spatial features that are favourable for the location of PV. More than 6900 parcels were analysed, in which 176 presented the highest value of the index of optimal PV location generated using the AHP method. This method provides a basis for further work by identifying optimal locations taking into account existing spatial conditions. The analyses carried out can be an important document in the future for spatial management, in particular for the location of new PV farms. As a continuation of the research, the authors will work on expanding the scope of the analyses and automating the entire process.
BIQ2021: a large-scale blind image quality assessment database

Perceptual quality assessment of digital images is becoming increasingly important due to widespread use of digital multimedia devices. Smartphones and high-speed internet are among the technologies that have increased the amount of multimedia content by several folds. Availability of a representative dataset, required for objective quality assessment training, is therefore an important challenge. We present a blind image quality assessment database (BIQ2021). The dataset addresses the challenge of representative images for no-reference image quality assessment by selecting images with naturally occurring distortions and reliable labeling. The dataset contains three set of images: images captured without intention of their use in image quality assessment, images obtained with intentional introduced natural distortions, and images collected from an open-source image sharing platform. Ensuring that the database contains a mix of images from different devices, containing different type of objects, and having varying degree of foreground and background information has been tried. The subjective scoring of these images is carried out in a laboratory environment through single-stimulus method to obtain reliable scores. The database provides details of subjective scoring, statistics of the human subjects, and the standard deviation of each image. The mean opinion scores (MOSs) provided with the dataset make it useful for assessment of visual quality. Moreover, existing blind image quality assessment approaches are tested on the proposed database, and the scores are analyzed using Pearson and Spearman’s correlation coefficients. The image database and the MOS along with relevant statistics are freely available for use and benchmarking.
Optical Navigation Method and Error Analysis for the Descending Landing Phase in Planetary Exploration

To solve the problem of high-precision optical navigation for the descent landing of lunar and planetary probes, an optical navigation method based on the spatial position distribution model is proposed. The method is based on crater detection, and an imaging cosine equivalent mathematical model based on the correspondence of crater objects is constructed. The geometric distribution of the probe spatial position is described to form an Abelian Lie group spatial torus to achieve absolute positioning for parametric optical navigation, Finally, the effect of the measurement error of crater detection on the positioning and attitude of the optical navigation system is discussed, with a fitted ellipse used as a typical analysis object. The effects of different crater distribution configurations and different detection errors on the performance of the proposed optical navigation algorithm are analyzed. The results of Monte Carlo simulation experiments showed that the algorithm proposed in this paper had the advantages of high stability, high accuracy, and good real-time performance, compared with existing methods.
The dynamics of governance capacity and legitimacy: the case of a digital tracing technology during the COVID-19 pandemic

Input, throughput, and output legitimacy of government measures are considered to be essential for governance capacity in crisis.During the COVID-19 crisis, governments around the world developed digital contacttracing applications to support their crisis management-with varying degrees of success.While Norway is seen as a high performer in the crisis, the contact-tracing app called Smittestopp developed in Norway had little impact.Using a case study, we studied the governance capacity and legitimacy of this technology in terms of how it was developed, how much it was utilized by citizens, and its usefulness relative to other government measures.Although the app did very little to help the COVID-19 crisis management in Norway, we identify some important lessons to be learned.We argue that the initial input and throughput legitimacy is important if a government policy is to maintain output legitimacy over time and be effective in a crisis.Consequently, this study contributes to the literature on governance capacity and legitimacy in crisis management.
Intelligent Fuzzy Control in Stabilizing Solar Sail with Individually Controllable Elements

Fuzzy logical control is a robust and effective control method in industrial fields, which renders it applicable to the attitude control of a solar sail. However, it is hard to apply in black-box and time-varying problem as real solar sail attitude control. Considering the lack of a priori knowledge and the unacceptable manual workload in the design of the fuzzy logical controller (FLC), an intelligent FLC designer (IFLCD) is developed by introducing neural network modelling and automatic design method. Besides, IFLCD also supports self-adaption for better control accuracy. By applying the proposed IFLCD in the attitude stabilization of a solar sail with individually controllable elements (SSICE), an effective solution of unmanned, time-varying, and complex system control method is offered without any mathematical model, which also overcomes the difficulties in FLC design Considering the performance degradation, accident, and distance problems faced by spacecraft, IFLCD can help with more practical problems that are hard be solved by traditional control theory.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
Generations of human-computer interactions: evolution, tendencies and perspectives

Abstract In this paper, we present the usability evolution of human-computer interactions. In addition, we group the various user interfaces of human-computer interaction into two generations with three classes each. The most user interfaces for human-computer interaction are assigned into a class depending on objective criteria scores, such as technical literacy level , level of natural interactions, user learning curve and UI’s ability to adapt. In addition, current tendencies for HCI are presented and future perspectives are discussed. Finally, we summarize the achieved results and draw conclusions.
A training strategy for hybrid models to break the curse of dimensionality

Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task. Given a set of binary labeled data, the challenge is to use them to develop a model that accurately assesses labels of new unlabeled data. Our strategy employs graph-theoretic methods to analyze the data and deduce a function that maps input features to output labels. Our focus here is on data sets represented by binary features in which the label assessment of unlabeled data points is always extrapolation. Our strategy shows the existence of small sets of data points within given binary data for which knowing the labels allows for extrapolation to the entire valid input space. An implementation of our strategy yields a notable reduction of training-data demand in a binary classification task compared with different supervised machine learning algorithms. As an application, we have fitted a tree-structured hybrid model to the vital status of a cohort of COVID-19 patients requiring intensive-care unit treatment and mechanical ventilation. Our learning strategy yields the existence of patient cohorts for whom knowing the vital status enables extrapolation to the entire valid input space of the developed hybrid model.
Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography

Radiologists consider fine-grained characteristics of mammograms as well as patient-specific information before making the final diagnosis. Recent literature suggests that a similar strategy works for Computer Aided Diagnosis (CAD) models; multi-task learning with radiological and patient features as auxiliary classification tasks improves the model performance in breast cancer detection. Unfortunately, the additional labels that these learning paradigms require, such as patient age, breast density, and lesion type, are often unavailable due to privacy restrictions and annotation costs. In this paper, we introduce a contrastive learning framework comprising a Lesion Contrastive Loss (LCL) and a Normal Contrastive Loss (NCL), which jointly encourage models to learn subtle variations beyond class labels in a self-supervised manner. The proposed loss functions effectively utilize the multi-view property of mammograms to sample contrastive image pairs. Unlike previous multi-task learning approaches, our method improves cancer detection performance without additional annotations. Experimental results further demonstrate that the proposed losses produce discriminative intra-class features and reduce false positive rates in challenging cases.
An Efficient Robot Exploration Method Based on Heuristics Biased Sampling

Autonomous robot exploration has received widespread attention as a crucial step in constructing maps for unknown environments. Among the research results in the field of robotic exploration, the boundary exploration algorithm based on rapidly exploring random tree (RRT) performs well in most scenarios, which guides the robot motion by taking the frontier with the maximum revenue value as the target point discretely. However, this discrete target selection scheme ignores the geometric continuity of the environment, and the robot often turns to another area before completing a geometrically continuous area (e.g., a complete room), which results in the disorderly motion of the robot and a relatively low exploration efficiency. This work proposes a heuristics biased sampling-based robot exploration strategy, which utilizes the semantic information of the environment as the heuristics to guide the robot exploration. First, a lightweight network model is proposed for heuristic object recognition. Second, a geometrically continuous region is estimated based on the heuristic object, and frontiers are then extracted in this region by using a biased sampling method. Finally, the heuristic information gain model is designed to determine the target frontier for the exploration, which instructs the robot to select the frontiers in the heuristic region with a higher priority. In this way, the robot can effectively use the heuristic knowledge of the environment to improve the efficiency of exploration. Compared with the RRT-based exploration methods in simulations and real-world environmental studies, and the experimental results prove the feasibility and effectiveness of our approach.
BoxPolyp: Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations

Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing polyp segmentation methods suffer from severe data shortage and impaired model generalization. Reversely, coarse polyp bounding box annotations are more accessible. Thus, in this paper, we propose a boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations. In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models, which generate fine-grained polyp area through the iterative boosted segmentation model. To achieve this goal, a fusion filter sampling (FFS) module is firstly proposed to generate pixel-wise pseudo labels from box annotations with less noise, leading to significant performance improvements. Besides, considering the appearance consistency of the same polyp, an image consistency (IC) loss is designed. Such IC loss explicitly narrows the distance between features extracted by two different networks, which improves the robustness of the model. Note that our BoxPolyp is a plug-and-play model, which can be merged into any appealing backbone. Quantitative and qualitative experimental results on five challenging benchmarks confirm that our proposed model outperforms previous state-of-the-art methods by a large margin.
GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

An automated segmentation and classification of nuclei is an essential task in digital pathology. The current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customized mixing mask, and combines them using the mask to generate a new rare-class nucleus. As it combines two nuclei, GradMix considers both nuclei and the neighboring environment by using the customized mixing mask. This allows us to generate realistic rare-class nuclei with varying environments. We employed two datasets to evaluate the effectiveness of GradMix. The experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.