Reviewerly/exHarmony huggingface dataset:
The concept of time and space in light of reflective activity of human brain

The subject of this research is defined by the following question: what allows a person to differentiate time phenomenon from all other; is inconsistency of materialistic world alone enough for encountering the time phenomenon; what are the sources of inseparable link between time with action and space; what is duration and how it differs from time; what is the substantiation for all properties assigned to time; what is the nature of qualities attributed to space? The author proposes an original approach towards solution of the problem of time: before speaking of one or another nature of time, it should be clearly realized which phenomenon is in question, what is the distinctive feature allowing its identification, and what are the form, way and conditions of its manifestation. The article determines an inseparable link of the time phenomenon with the function that people unconsciously assign in the course of their practical activity to sequence of occurrences, formed by the shift in current states of rotation of the Earth. Active role of brain of the subject in &amp;ldquo;organization&amp;rdquo; of time phenomenon is revealed alongside. The affiliation of the concept of time to the range of concept-statuses alongside general specificity of the objective concept of such concepts are determined. The conclusion is made that there was a time when time did not exist. The author provides definitions to the fundamental time concepts based on the revealed objective content of these concepts. The fact is stated on the emergence of a completely new concept of time that gives the key to unraveling all of its mysteries. &amp;nbsp;
Twelve Agendas on Interacting with Information: A Human-Engaged Computing Perspective

During the coronavirus global pandemic crisis, we have received information from authentic and inauthentic sources. Fake news, continuous rumors, and prejudiced opinions from digital platforms and social media have the capacity to disrupt social harmony, to stall personal development, and to undermine trust on all levels of human interaction. Despite the wide plurality of perspectives, the diversity of contents, the variety of voices, and the many often-conflicting reasons for publishing, our interactions with information on digital devices are progressively shaping such situations and affecting decisions on all levels. We look at the limitations of existing designs and guidelines in the current paradigm, and we ask to what extent researchers and developers can focus and contribute, through their innovations, to the reduction of uncertainty and cases of misdirection, how they can mitigate tensions between information and humans, and how they can contribute to the maintenance and enhancement of worthy human values. Human-engaged computing (HEC) calls for innate user capacities to be enhanced rather than displaced by digital technologies so that the human factor in interactions is fully exploited and truly efficient symbiotic relationships between humans and devices can be achieved. Under the framework of HEC, we propose 12 research agendas from
Learning Privately with Labeled and Unlabeled Examples

A private learner is an algorithm that given a sample of labeled individual examples outputs a generalizing hypothesis while preserving the privacy of each individual. In 2008, Kasiviswanathan et al. (FOCS 2008) gave a generic construction of private learners, in which the sample complexity is (generally) higher than what is needed for non-private learners. This gap in the sample complexity was then further studied in several followup papers, showing that (at least in some cases) this gap is unavoidable. Moreover, those papers considered ways to overcome the gap, by relaxing either the privacy or the learning guarantees of the learner. We suggest an alternative approach, inspired by the (non-private) models of semi-supervised learning and active-learning, where the focus is on the sample complexity of labeled examples whereas unlabeled examples are of a significantly lower cost. We consider private semi-supervised learners that operate on a random sample, where only a (hopefully small) portion of this sample is labeled. The learners have no control over which of the sample elements are labeled. Our main result is that the labeled sample complexity of private learners is characterized by the VC dimension. We present two generic constructions of private semi-supervised learners. The first construction is of learners where the labeled sample complexity is proportional to the VC dimension of the concept class, however, the unlabeled sample complexity of the algorithm is as big as the representation length of domain elements. Our second construction presents a new technique for decreasing the labeled sample complexity of a given private learner, while roughly maintaining its unlabeled sample complexity. In addition, we show that in some settings the labeled sample complexity does not depend on the privacy parameters of the learner.
Analysis of face detection based on skin color characteristic and AdaBoost algorithm

Abstract For the face detection method based on skin color feature and AdaBoost algorithm, if one of them is used to detect the face, it can also catch the face to a certain extent. However, the detection rate and an error rate of this single method in its detection experiment can’t achieve good results. Therefore, this paper combines the advantages of the two algorithms, combines the two approaches, and improves them. The main idea is to use the skin color features of face detection as pre-detection, and use the established skin color distribution Gaussian model to obtain candidate regions containing the skin color of the face, and then use a cascade classifier to detect the skin color regions. By using OpenCV and Visual Studio software, a lot of experimental statistics and analysis are carried out. The research shows that the improved algorithm is superior to the two algorithms in detection rate and false detection rate, and it can also achieve a good detection effect for the face in a complicated situation.
RGB-D Vision Device for Tracking a Moving Target

Computer vision for control is a growing domain of research and it is widespread in industry and autonomous vehicle field. Towards the applicability of such approach the development of proper algorithms to interpret vision data is mandatory. Here, we propose an application to drive the movement of a mechanical system for target object tracking through an RGB-depth camera aid.
FHI: A Fault Intensity-based Hierarchical Association Analysis Model for Mining Fault Database of Railway OCS

The overhead contact system (OCS) is the power source of electrified railway, it is very important to maintain the OCS safely, stably and efficiently. At present, reliability analysis is the main method to study OCS fault data. Most of fault trees include only part of components of OCS. However, there are few studies from system-wise perspective. This paper applies data mining to OCS for the first time and proposes a hierarchical association analysis model based on fault intensity (FHI) according to the characteristics of OCS fault data (sparsity and hierarchy). Firstly, we proposed method of multi-dimensional partition (MDP) to transform fault records to horizontal data format, which can reduce the sparsity of data. Secondly, we defined the fault intensity to replace the threshold of support and introduced adjustment factor to realize it, which can eliminate the influence of unequal partition and make the mining result more reasonable. Thirdly, we proposed a new pruning strategy to mine cross-level association rules. By mining the historical OCS fault data collected from high speed railway lines of entire province in north-west China, we verify the validity of this analysis model and reveal the inner connections between faults. Based on the analysis results, the detailed suggestions are given to guide the operation and maintenance of ocs.
Object Detection of Remote Sensing Airport Image Based on Improved Faster R-CNN

Abstract In order to effectively improve the detection accuracy of remote sensing images in airport areas, basing on the representative deep network Faster R-CNN as the object detection method, a deeper basic network ResNet and feature fusion component FPN are used to extract more robust deep distinguishing features, and add a new fully connected layer to the end detection network and combine the softmax classifier and 4 logistic regression classifiers for object detection according to the inter-class correlation of the object. Experiments show that the improvement of the original network brings a 7.7% mAP improvement to 76.6% of the mAP. Compared with other mainstream networks, it also has a better accuracy rate. At the same time, by appropriately reducing the input amount of the proposals, the speed can be increased 3 times to 0.169s under the premise of reducing the accuracy by 2.2%. According to the specific task, the accuracy and detection speed can be reasonably weighed, which reflects the effectiveness and practicability of the network.
Secret sharing schemes based on Nonlinear codes

Secret sharing scheme is a method in which the secret is divided among finitely many participants by a dealer such that only the legitimate set of participants can recover the secret. The collection of sets of legitimate participants is called the access structure of the secret sharing scheme. There are various ways of constructing secret sharing schemes. Determination of the access structure for a secret sharing scheme is an important problem. Most of the known secret sharing schemes are based on linear codes. A major drawback of secret sharing schemes based on linear codes is that these schemes are susceptible to Tompa- Woll attack. In this paper, we use nonlinear codes to construct secret sharing schemes. These secret sharing schemes perform better than the secret sharing schemes based on linear codes with respect to Tompa-Woll attack.
Signal recovery with a limited number of ideal discrete samples

The article discusses the restoration of continuous signals from a limited set of discrete values.The spectrum of discrete values is a periodically repeating spectrum of a continuous signal.Kotelnikov's theorem determines the conditions under which the spectrum of a single signal can be distinguished without distortion, and then an original signal can be restored from them.However, interpolation using a series implies an infinite number of samples.The article shows that when the Nyquist conditions are fulfilled, signal restoration with a limited number of discrete values using the interpolation series gives significant errors.However, if the signal is periodic, it is possible to completely restore the signal from a limited set of sampling points.In this case, the discretization process can be represented as the action of a limited Dirac lattice.A simple procedure is proposed for reconstructing the original signal which consists of a discrete Fourier transform from the samples, complementing the spectrum with zeros to the range of the selected signal, and the inverse Fourier transform.For a non-periodic signal, complete reconstruction from discrete points does not occur, even if the conditions of the Kotelnikov theorem are satisfied when the initial function is discretized.This is due to the overlapping of single spectra as a result of convolution with the Fourier transform of a rectangular pulse which cuts out a non-periodic signal from a periodic one.Each single spectrum becomes infinite as a result of the convolution with the Fourier transform of the cutting rectangular pulse.It is not possible to precisely single out a single spectrum of a continuous signal.This leads to an error in the restoration of the original signal.The error can be reduced by reducing the sampling interval or by interpolating the edges of the selected single pulse.
An Alphabet-Size Bound for the Information Bottleneck Function

The information bottleneck function gives a measure of optimal preservation of correlation between some random variable X and some side information Y while compressing X into a new random variable W with bounded remaining correlation to X. As such, the information bottleneck has found many natural applications in machine learning, coding and video compression. The main objective in order to calculate the information bottleneck is to find the optimal representation on W. This could in principle be arbitrarily complicated, but fortunately it is known that the cardinality of W can be restricted as |W| ≤ |X |+1 which makes the calculation possible for finite |X|. Now, for many practical applications, e.g. in machine learning, X represents a potentially very large data space, while Y is from a comparably small set of labels. This raises the question whether the known cardinality bound can be improved in such situations. We show that the information bottleneck function can always be approximated up to an error δ(ε, |Y|) with a cardinality |W| ≤ f(ε, |Y|), for explicitly given functions δ and f of an approximation parameter c> 0 and the cardinality of Y. Finally, we generalize the known cardinality bounds to the case were some of the random variables represent quantum information.
Mediating Effect of Intrapreneurship on Psychological Ownership and Teachers’ In-Role Performance

This paper investigated the mediating effect of intrapreneurship on the relationship between psychological ownership and teachers’ in-role performance in private secondary schools in Kano, Nigeria. The data for the study was collected through the use of questionnaire. A total of 338 teachers from selected private secondary schools in Kano metropolis were used for the study. The study used Statistical Package for Social Science (SPSS) Version 22 and PLSSEM Version 3 for data screening and path structuring modelling, respectively. The study reveals positive significant effect of psychological ownership on teachers’ in-role performance. Importantly, the study findings provided empirical evidence that intrapreneurship mediates the relationship between organisational-based psychological ownership and teachers’ role performance. The study recommends that the in-role performance of teachers can be enhanced when the teachers have the feeling of psychological ownership of their respective schools. Similarly, if teachers have psychological ownership for their respective schools, they would be motivated to be more innovative which consequently could improve performance of their basic responsibilities in the school. Direction for future study was also discussed.
Fair-MDAV: An Algorithm for Fair Privacy by Microaggregation

Automated decision systems are being integrated to several institutions. The General Data Protection Regulation from the European Union, considers the right to explanation on such decisions, but many systems may require a group-level or community-wide analysis. However, the data on which the algorithms are trained is frequently personal data. Hence, the privacy of individuals should be protected, at the same time, ensuring the fairness of the algorithmic decisions made. In this paper we present the algorithm Fair-MDAV for privacy protection in terms of t-closeness. We show that its microaggregation procedure for privacy protection improves fairness through relabelling, while the improvement on fairness obtained equalises privacy guarantees for different groups. We perform an empirical test on Adult Dataset, carrying out the classification task of predicting whether an individual earns $$\$50,000$$ per year, after applying Fair-MDAV with different parameters on the training set. We observe that the accuracy of the results on the test set is well preserved, with additional guarantees of privacy and fairness.
Design of a WeChat Mobile Learning Platform for Multi-modal Language Learning and its Application

Abstract To follow the increasingly popularity of MALL (Mobile Assisted Language Learning) in the mobile multi-media era, the study probes into the design of a multi-modal language mobile learning platform supported by WeChat. The study also practically applies the platform to an English course for mobile learning and analyses the learning results with the quasi experimental research method. The experiment shows that the platform can effectively improve learners’ language ability, especially listening, writing and speaking skills.
Trend-aware motion planning for wheeled mobile robots operating in dynamic environments

Planning collision-free trajectories is essential for wheeled mobile robots operating in dynamic environments safely and efficiently. Most current trajectory generation methods focus on achieving optimal trajectories in static maps and considering dynamic obstacles as static depending on the precise motion estimation of the obstacles. However, in realistic applications, dealing with dynamic obstacles that have low reliable motion estimation is a common situation. Furthermore, inaccurate motion estimation leads to poor quality of motion prediction. To generate safe and smooth trajectories in such a dynamic environment, we propose a motion planning algorithm called trend-aware motion planning (TAMP) for dynamic obstacle avoidance, which combines with timed-elastic band. Instead of considering dynamic obstacles as static, our planning approach predicts the moving trends of the obstacles based on the given estimation. Subsequently, the approach generates a trajectory away from dynamic obstacles, meanwhile, avoiding the moving trends of the obstacles. To cope with multiple constraints, an optimization approach is adopted to refine the generated trajectory and minimize the cost. A comparison of our approach against other state-of-the-art methods is conducted. Results show that trajectories generated by TAMP are robust to handle the poor quality of obstacles’ motion prediction and have better efficiency and performance.
Fast Approximate Multioutput Gaussian Processes

Gaussian processes regression models are an appealing machine learning method as they learn expressive nonlinear models from exemplar data with minimal parameter tuning and estimate both the mean and covariance of unseen points. However, cubic computational complexity growth with the number of samples has been a long standing challenge. Training requires the inversion of $N \times N$N×N kernel at every iteration, whereas regression needs computation of an $m \times N$m×N kernel, where $N$N and $m$m are the number of training and test points, respectively. This work demonstrates how approximating the covariance kernel using eigenvalues and functions leads to an approximate Gaussian process with significant reduction in training and regression complexity. Training now requires computing only an $N \times n$N×n eigenfunction matrix and an $n \times n$n×n inverse, where $n$n is a selected number of eigenvalues. Furthermore, regression now only requires an $m \times n$m×n matrix. Finally, in a special case, the hyperparameter optimization is completely independent from the number of training samples. The proposed method can regress over multiple outputs, learn the correlations between them, and estimate their derivatives to any order. The computational complexity reduction, regression capabilities, multioutput correlation learning, and comparison to the state of the art are demonstrated in simulation examples. Finally we show how the proposed approach can be utilized to model real human data.
HDRF

The selection of data reduction schemes, which is crucial for reduced data footprints on a distributed file system (DFS) and transferring big data, is usually limited to the schemes supported by the underlying platforms. If the source code of the platform is available, it might be possible to add user-favorite reduction schemes, but it requires an expensive implementation cost or is virtually impossible. In this paper, we propose a system design that links a DFS to reduction schemes and enables them transparently to data processing applications. To verify the feasibility of this approach, we implemented a framework within Hadoop DFS (HDFS) named Hadoop Data Reduction Framework (HDRF). The features of HDRF is threefold. First, the application programmers can easily incorporate their favorite reduction schemes with reasonably restrained implementation costs. Second, the selection is transparent to data processing applications. Lastly, experimental results show that HDRF can halve the vanilla HDFS transfer time by using a more optimized application without compromising the compression ratio. When running the same application, HDRF has minimal runtime and storage overheads compared to vanilla HDFS.
A Cramér–von Mises Test of Uniformity on the Hypersphere

Testing uniformity of a sample supported on the hypersphere is one of the first steps when analysing multivariate data for which only the directions (and not the magnitudes) are of interest. In this work, a projection-based Cramér–von Mises test of uniformity on the hypersphere is introduced. This test can be regarded as an extension of the well-known Watson test of circular uniformity to the hypersphere. The null asymptotic distribution of the test statistic is obtained and, via numerical experiments, shown to be tractable and practical. A novel study on the uniformity of the distribution of craters on Venus illustrates the usage of the test.
Research on the Development of Leisure Sports in Coastal Cities Based on Ecological Environment

Yang, J., 2020. Research on the development of leisure sports in coastal cities based on ecological environment. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 166-168. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the comprehensive benefit of coastal urban coastal protection, we need to carry out a sound environmental protection design of the coastal urban buildings, so as to adjust the scientific environmental protection coordination of every link in the coastal urban buildings. with the acceleration of urbanization in our country, we also put forward higher requirements for the environmental protection design of buildings. under the background of coordinated development, we analyze the many factors that affect the environmental protection of buildings at present, and give reasonable solutions to these problems, so as to lay a certain foundation for the future development of buildings in our country. This paper focuses on the research on the development of leisure sports in coastal cities from the perspective of ecological environment, hoping to bring some help to the development of ecological protection in coastal cities.
Intentional Image Similarity Search

Image representations learned by deep convolutional neural networks (CNNs) have greatly improved the performance of content-based image retrieval systems in recent years. Query-by-example is the most popular strategy to represent a user’s search intention in content-based image and video retrieval scenarios. Nevertheless, simply presenting an image as a query is often insufficient to express a user’s intention, due to ambiguities in the query image. To better meet a user’s search intention, a novel intentional image similarity search approach is proposed, consisting of a scheme for specifying a user’s intention for a query image, a plugin mechanism to support more fine-grained neural network models for specific search, and a hybrid feature method based on CNN and handcrafted features. Furthermore, a novel analysis technique for deep similarity networks is introduced for the purpose of finding relevant image regions. The proposed approach is evaluated qualitatively on video recordings of the German Broadcasting Archive.